# AgentCF: Collaborative Learning with Autonomous Language Agents for
Recommender Systems

Junjie Zhang1, Yupeng Hou2, Ruobing Xie3, Wenqi Sun1, Julian McAuley2, Wayne
Xin Zhao1,🖂 and  Leyu Lin3, and Ji-Rong Wen1<EMAIL_ADDRESS><EMAIL_ADDRESS>1Gaoling School of Artificial Intelligence, Renmin
University of China2UC San Diego3WeChat, Tencent

(2022)

###### Abstract.

Recently, there has been an emergence of employing LLM-powered agents as
believable human proxies, based on their remarkable decision-making
capability. However, existing studies mainly focus on simulating human
dialogue. Human non-verbal behaviors, such as item clicking in recommender
systems, although implicitly exhibiting user preferences and could enhance the
modeling of users, have not been deeply explored. The main reasons lie in the
gap between language modeling and behavior modeling, as well as the
incomprehension of LLMs about user-item relations.

To address this issue, we propose AgentCF for simulating user-item
interactions in recommender systems through agent-based collaborative
filtering. We creatively consider not only users but also items as agents, and
develop a collaborative learning approach that optimizes both kinds of agents
together. Specifically, at each time step, we first prompt the user and item
agents to interact autonomously. Then, based on the disparities between the
agents’ decisions and real-world interaction records, user and item agents are
prompted to reflect on and adjust the misleading simulations collaboratively,
thereby modeling their two-sided relations. The optimized agents can also
propagate their preferences to other agents in subsequent interactions,
implicitly capturing the collaborative filtering idea. Overall, the optimized
agents exhibit diverse interaction behaviors within our framework, including
user-item, user-user, item-item, and collective interactions. The results show
that these agents can demonstrate personalized behaviors akin to those of
real-world individuals, sparking the development of next-generation user
behavior simulation.

Agents, Large Language Models, Collaborative Learning

🖂 Corresponding author.

††copyright: acmcopyright††journalyear: 2022††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation email; August 03–05, 2022; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Information systems Recommender
systems

##  1\. Introduction

With the recent advancements in large language models (LLMs), LLM-powered
agents demonstrate impressive capabilities in autonomous interaction and
decision making (Zhao et al., 2023; Wang et al., 2023e; Li et al., 2023a; Lin
et al., [n. d.]). By scaling their numbers, these agents exhibit the emergence
of human-like behaviors at both the individual and population levels (Gao et
al., 2023a; Park et al., [n. d.]). This observation highlights the potential
of employing LLM-powered agents to simulate human social behaviors, such as
daily lives in Smallville (Park et al., [n. d.]) and software development
(Qian et al., [n. d.]).

Typically, existing studies primarily focus on simulating human dialogue using
semantic knowledge of LLMs. However, in addition to dialogue, real-world human
behaviors also involve non-verbal aspects like user-item interactions in
recommender systems, which implicitly reflect user preferences and have the
potential to facilitate personalized user modeling. To simulate such
behaviors, some work verbalizes these interaction records into natural
language text to prompt LLMs (Wang et al., 2023e, f). Nevertheless, we argue
that this method struggles to capture the underlying behavioral patterns of
these interactions, due to the gap between universal language modeling and
personalized behavior modeling. For example, shoppers who buy diapers on
Friday also tend to buy beer (Padmanabhan and Tuzhilin, 1999). This behavioral
pattern can be effectively captured by collaborative filtering models (He et
al., 2017; Rendle et al., 2009), but confuse LLMs as the two items are
semantically unrelated. Therefore, it is crucial to develop methods to better
characterize human behaviors in LLM-powered simulations.

Focused on this research topic, in this work, we take recommender systems, a
common web application, as the testbed to study the simulation of user-item
interactions with LLM-power agents. Typically, in addition to directly
prompting LLMs based on user historical interactions (Wang and Lim, 2023; Dai
et al., 2023), there are also some studies that employ agents to perform
recommendations, by introducing specialized tools and planning strategies
(Wang et al., 2023b; Huang et al., 2023). Especially, RecAgent (Wang et al.,
2023f) develops an LLM-based simulator, regarding users as agents. However,
these studies mainly focus on characterizing user-side behaviors using
universal LLMs, neglecting the effect of item-side modeling in this two-sided
interaction process. In recommender systems, users possess personalized
preferences for items, while items have potential adopters. Modeling the two-
sided relations between users and items is crucial for enhancing personalized
recommendations (He et al., 2017).

Considering these issues, our solution is inspired by the _collaborative
learning_ idea from previous recommendation models (_e.g.,_ BPR (Rendle et
al., 2009) and NCF (He et al., 2017)). In these methods, users and items are
modeled as equally important parts and the recommender is essentially a
preference function that measures user-item affinity based on the two sides.
During the optimization process, the parameters related to users and items are
collaboratively optimized, making the recommender better fit interaction
records. Therefore, to facilitate the comprehension of user-item relations by
LLM-powered agents, our idea is to simulate both _user agents_ and _item
agents_ , and mimic the optimization process of traditional recommenders. The
merits of such a method are twofold. Firstly, it can model the user-item
interaction by autonomous interaction between user and item agents, instead of
verbalizing the interaction as plain text (Wang et al., 2023f). Secondly, it
enables mutual optimization of user and item agents to capture their two-sided
relations, rather than simulating user behaviors individually. We are also
aware of several studies, such as Reflexion (Shinn et al., 2023) and Self-
Refine (Madaan et al., 2023), optimize general-purpose agents for better
decision-making in downstream tasks. However, these methods are task-focused
or user-focused, optimizing agents through their own exploration, which is
essentially _self-learning_ rather than _collaborative learning_ emphasized in
our approach.

To this end, we propose the agent-based collaborative filtering approach,
named AgentCF. Unlike previous studies that mainly focus on user simulation,
our approach considers not only users but also items as agents. Both kinds of
agents are equipped with memory modules, maintaining the simulated preferences
and tastes of potential adopters. This involves their intrinsic features as
well as acquired behavioral information. As the key of our approach, we
collaboratively optimize the user agents and item agents, leveraging the
remarkable decision-making and reflection abilities of LLMs. At each step, we
prompt user and item agents to autonomously interact, thereby exploring
whether these simulated agents can make consistent decisions with real-world
interaction records. Then, based on the feedback obtained from these
interactions, we design a collaborative reflection mechanism that enables user
agents and item agents to reflect on and adjust their memory in a mutual
manner. In this way, the simulated preferences of user agents and item agents
mutually aggregate, and can be propagated to other agents in subsequent
interactions. Thus, it implicitly models the collaborative filtering idea
through interactions.

We evaluate our proposed approach through extensive experiments on real-world
datasets. Results show that our approach effectively simulates user-item
interactions, achieving promising performance on recommendation tasks compared
to several classical recommendation models and LLM-based recommenders.
Moreover, within our framework, user and item agents engage in free
communication, creating various types of interactions (user-user, item-item,
and collective), wherein they exhibit personalized behaviors similar to those
of real-world individuals. The results also spark the development of next-
generation user behavior simulation. The main contributions of this work are
as follows:

$\bullet$ We consider both users and items as agents, and develop a user-item
interaction simulation approach for recommender systems, emphasizing the
modeling of two-sided interaction relations.

$\bullet$ We collaboratively optimize the user and item agents, design a
collaborative reflection mechanism, and employ it to achieve mutual update of
user and item memory.

$\bullet$ Extensive experiments demonstrate the effectiveness of our approach
in simulating personalized interactions.

##  2\. Methodology

In this section, we present the proposed agent-based collaborative filtering
approach, named AgentCF. Our approach enables user agents and item agents to
collaboratively learn from user-item interactions in recommender systems. The
overall framework of our proposed AgentCF is depicted in Figure 1.

###  2.1. Preliminaries

Figure 1. The overall framework of AgentCF and a case about the optimization
process of agents: (1) The user and item agents are first prompted to
autonomously interact. (2) These agents adjust the misconceptions in their
memory, by reflecting on the disparities between their decisions and real-
world interactions. In this process, the simulated preferences of user and
item agents aggregate (as indicated by the highlighted content) and can
propagate to other agents in subsequent interactions.

To ease the understanding of our approach, we first describe the traditional
recommendation setting, and then introduce our task setting and the overview
of our approach.

Traditional Recommendation Setting. In a recommender system, there exists a
set of users $\mathcal{U}=\\{u\\}$, a set of items $\mathcal{I}=\\{i\\}$, and
a set of their interaction records (often grouped by users in chronological
order), denoted by $\mathcal{D}=\\{\langle u,i\rangle\\}$. To conduct
recommendation, a preference function $f(u,i)$ is built to capture the
preference degree of user $u$ over item $i$, where an interacted item (_i.e.,_
positive item, denoted by $i^{+}$) would receive a higher score than an item
that the user does not interact with (_i.e.,_ negative item, denoted by
$i^{-}$). For example, BPR (Rendle et al., 2009) builds a personalized
pairwise ranking model that compares the preference over two candidate items,
and NCF (He et al., 2017) employs neural networks to fit user-item interaction
relationship. Subsequently, the learning of preference function $f(u,i)$ can
be converted into a standard gradient-based function fitting problem based on
training data $\mathcal{D}=\\{\langle u,i\rangle\\}$ in machine learning
(Bottou, 2010).

Our Task Setting. In this work, we follow the traditional recommendation
setting above and incorporate LLM-powered agents into recommender systems.
Different from prior studies (Wang et al., 2023f; Park et al., [n. d.]) only
considering task or user agents, _item agents_ are also included in our work
and will be a crucial part of developing our approach. Notably, we slightly
reuse the notations, and use $u$ and $i$ to denote a user agent and an item
agent, respectively. Specifically, a user agent $u$ is expected to capture the
preference of the corresponding real user, and an item agent $i$ is expected
to reflect the corresponding item characteristics and potential adopters’
preferences. Following such a setting, the original user-item interaction will
be simulated by autonomous interaction between the user and item agents. In
our work, we consider a _ranking_ task that ranks a candidate list
$\\{c_{1},\cdots,c_{n}\\}$ for a user agent $u$ based on LLMs:
$f_{\mathit{LLM}}(u,\\{c_{1},\cdots,c_{n}\\})$. Different from traditional
recommendation models, the LLM that implements $f_{\mathit{LLM}}(\cdot)$ will
be fixed during the optimization process.

Overview of AgentCF. To implement the agents, the memory mechanism (_i.e.,_
storing the past states, actions, and contexts (Wang et al., 2023c)) and the
reflection mechanism (_i.e.,_ revising the agents’ states or recognitions
(Shinn et al., 2023; Pan et al., 2023)) have been widely explored in various
task scenarios. However, existing work often employs a task-focused (_e.g.,_
ReAct (Yao et al., 2023)) or user-oriented simulation approach (_e.g.,_
RecAgent (Wang et al., 2023f)), and the objects (_e.g.,_ the items in
recommender systems) involved in this process are not explicitly considered.
Since the recommendation task essentially needs to model the two-sided
interaction relation between users and items, we argue that item agents are
important to simulate the real interaction scenario in recommender systems.
Without incorporating item agents, it is difficult to explicitly model the
essential idea “ _like alike_ ” (_i.e.,_ like the items that are adopted by
similar users) in collaborative filtering (Breese et al., 2013). Notably, our
approach has two technical contributions in adapting autonomous language
agents for recommender systems:

$\bullet$ _Collaborative memory-based optimization_. Instead of simply
prompting LLMs with user’s historical interactions (Dai et al., 2023; Liu et
al., 2023) , we aim to refine (without gradient update) both the simulated
user and item agents through their mutual interactions. At each time step,
these agents are prompted to conduct autonomous interactions, and then
collaboratively reflect on the disparities between their decisions and real-
world interaction records. In this way, the simulated user and item agents can
accordingly adjust or update their memories, enabling them to better fit the
real-world interaction behaviors.

$\bullet$ _Implicit preference propogation_. Different from prior studies
(Wang et al., 2023f; Qian et al., [n. d.]), a notable feature of our approach
is that we update the memory of both users and item agents for each
interaction record. This enables the item memory to be injected into the
preferences of users who interact with it. When new interactions occur,
subsequent users will be also informed about the preference of previous
adopters of this item. The same process can also be applied to the user side.
In this case, our approach essentially models the collaborative filtering idea
by propagating preferences from user-item interactions.

###  2.2.  Collaborative Agent Optimization

In this part, we present the collaborative optimization process that is
developed based on both user and item agents, and discuss how it relates to
the classic collaborative filtering approach.

####  2.2.1. Memory Design

To specialize the LLM-powered agents for recommender systems, different from
prior studies (Wang et al., 2023f; Park et al., [n. d.]), we equip both user
and item agents with memory modules, maintaining their intrinsic features and
collaborative information.

User Memory. For user agents, the memory module aims to store various kinds of
useful information that reflects the user preferences. Since real-world user
preferences often change dynamically, we equip each user agent $u$ with short-
term memory $M_{u}^{s}$ and long-term memory $M_{u}^{l}$. Especially, short-
term memory is a natural language text that describes the recently updated
preference of the user agent, which can be initialized with their general
preferences like “ _I enjoy listening to CDs_ ”. Furthermore, long-term memory
is a pool of historical preference texts that can store the evolving process
of user preference. When engaged in a new interaction, user agents can
directly access their short-term memory, while retrieving relevant information
from their long-term memory.

Item Memory. For item agents, we equip them with adjustable memory modules to
record information about their own characteristics as well as the preferences
of their adopters, _e.g.,_ a rock CD may be ideal for energetic guitar-driven
enthusiasts. However, unlike user memory, we only equip each item agent $i$
with a unified memory module $M_{i}$, since item information is relatively
stable through time. Item memory can be initialized by their identity
information, such as titles and categories, and will be continuously updated
with user preferences when making new interactions. This process can largely
complete the global characteristics of an item in real-world systems and
enable the propagation of preference information, which is the key to
collaborative learning in our approach.

####  2.2.2. Autonomous Interactions for Contrastive Item Selection

Given the initialized agents, our task is to optimize these agents and enable
them to simulate real-world user-item interactions. To achieve this, we first
explore the behavior alignment between these simulated agents and real-world
individuals, so as to provide feedback for updating these agents. This
involves testing whether the agents can make consistent decisions with real-
world interaction records when selecting candidates. We can also consider this
process as the “ _forward computation_ ” of the recommender models (_e.g.,_
BPR (Rendle et al., 2009)) when comparing the preference over two items by a
user.

Specifically, we employ the real user behavioral sequences (arranged in
chronological order) as “training data”. At each step of these interactions,
we present a contrastive pair of both a positive item $i^{+}$ and a negative
item $i^{-}$ as candidates for user agents to select from. Intuitively, agents
lacking personalization mainly rely on commonsense knowledge to select popular
candidates and those positioned higher in the display list for interaction. To
increase the discrimination difficulty, we deliberately introduce popular bias
and position bias in candidate selection: we sample a negative item with high
popularity in the dataset and then place it before the positive item in the
selection list. Then, the user agent $u$ is tasked with selecting an item
$i^{o}$ from the candidates and providing explanations $y_{exp}$ for this
choice, by collaboratively considering the preference from its memory and the
features from the memories of candidates:

(1) $\displaystyle i^{o}$ $\displaystyle=f_{\mathit{LLM}}(M_{u};\ M_{i^{-}};\
M_{i^{+}})$ (2) $\displaystyle y_{\mathit{exp}}$
$\displaystyle=\text{Prompt}_{\mathit{LLM}}(i^{o};\ M_{u};\ M_{i^{-}};\
M_{i^{+}})$

####  2.2.3. Collaborative Reflection and Memory Update

The above process has enabled agents to simulate the interaction behaviors.
Next, we derive the feedback signal by comparing the agents’ decisions with
real-world interaction data, and employ it to collaboratively optimize the
involved user agent and item agents, serving as the “backward update” part of
traditional recommenders. Since LLMs are fixed in this work, there is no
explicit gradient-based learning process as in traditional recommender models
(Rendle et al., 2009). Thus, we mainly update the associated memories of user
and item agents.

Based on the agent’s decision $i^{o}$ and explanation $y_{\mathit{exp}}$
generated above, we prompt both user and item agents to reflect on and adjust
the misconceptions in their simulated preferences and features. We refer to
this process as _collaborative reflection_ , as it emphasizes the
collaborative learning of the user-item relations, by performing reflection
based on both user and item memories. It is different from task-specific
reflection (_e.g.,_ ReAct (Yao et al., 2023)) and user-oriented reflection
(_e.g.,_ RecAgent (Wang et al., 2023f)), which construct the _self-reflection_
only from the perspective of the task solver or user. Specifically, if a user
agent makes the right choice that aligns with the real behavior, we inform it
about the correctness and store the related interaction information in its
memory. For an incorrect choice, we introduce the following collaborative
reflection mechanism to revise agents’ memories and behaviors accordingly,
enabling user agents to simulate real interaction behaviors and the involved
item agents to align with the adopter’s preferences:

(3) $\displaystyle{M^{s}_{u}}^{\prime}$
$\displaystyle\leftarrow\text{Reflection}^{u}(i^{o};\ y_{\mathit{exp}};\
M_{u};\ M_{i^{-}};\ M_{i^{+}}),$ (4) $\displaystyle M^{\prime}_{i}$
$\displaystyle\leftarrow\text{Reflection}^{i}(i^{o};\ y_{\mathit{exp}};\
M_{u};\ M_{i^{-}};\ M_{i^{+}})$ (5) $\displaystyle{M^{l}_{u}}^{\prime}$
$\displaystyle\leftarrow\text{Append}(M^{l}_{u};\ M^{s}_{u}),$

where ${M^{s}_{u}}^{\prime}$ denote the reflected short-term memory of user
$u$ and $M^{\prime}_{i}$ denote the reflected memory of item $i$.
${M^{l}_{u}}^{\prime}$ is the updated long-term memory of user $u$, which
appends the user’s previous short-term memory and can be retrieved when making
a new interaction. Note that we do not modify the memory of the negative item
agent, because we find that LLMs tend to over-complain the negative item
agent’s drawbacks, disregarding the fact that it may also be attractive for
other users. From Equation (3) and (4) (a similar prompting method in Equation
(2)), we can see that the reflection is actually derived based on the memories
of user agent $u$, positive item agent $i^{+}$, and negative item agent
$i^{-}$. Therefore, such a reflection mechanism enables user and item agents
to better understand their two-sided interaction relations by effectively
discriminating between positive instances and negative instances.

At each step of interaction (_i.e.,_ optimization), we iterate the selection
process (Section 2.2.2) and the collaborative reflection process (Section
2.2.3) until user agents make choices consistent with those of real users or
reach the maximum round of iterations. In essence, throughout the
collaborative optimization process, the simulated user and item agents follow
the decision-making process of real-world individuals, progressing step by
step. This makes user agents become more personalized and item agents
aggregate the preferences of adopters by learning from interactions, improving
the simulation of user-item interactions. The implementation details and
prompts are presented in Appendix B and C.

####  2.2.4. Connection with Classical Recommendation Models

In the field of recommender systems, various models such as BPR (Rendle et
al., 2009) and NCF (He et al., 2017) are developed. In general, these models
set parameters (typically hidden embeddings) to represent user preferences and
item characteristics, and optimize them to fit user-item interaction records
through two stages, namely _forward preference evaluation_ (estimating
preference score) and _backward parameter update_ (performing gradient
update). In this way, users or items that display similar interaction
behaviors can have similar representations in parameter space, thus capturing
the collaborative filtering idea.

Our approach mimics the optimization process of traditional recommenders. The
user and item memories can be considered as their language-based parameters
(_i.e.,_ embeddings). The item selection process (Section 2.2.2) and
collaborative reflection process (Section 2.2.3) correspond to the forward and
backward stages in recommendation models. In our approach, explicit gradient
optimization is not employed. Instead, the collaborative reflection plays a
similar role of “ _semantic gradient_ ”, steering how user and item agents
adjust themselves. Moreover, based on the collaborative reflection, the user
and item agents mutually interact and aggregate each other’s preference
information, which is then propagated to new agents in subsequent
interactions. Therefore, by establishing preference propagation from
interactions of user and item agents, our approach incorporates the idea of
collaborative filtering.

###  2.3. Agent Interaction Inference

After the above memory-based optimization, our approach can simulate highly
personalized user agents and preference-aware item agents. In this part, we
further study how to employ these simulated agents to infer the potential
user-item interactions, focusing on the ranking task for a list of candidates
$\\{c_{1},\cdots,c_{n}\\}$.

Basic Prompting Strategy. Since both user and item agents are collaboratively
optimized to model their two-sided relations, as the basic prompting form, we
directly prompt the LLM with the agents’ simulated user preferences and the
candidate item features. This enables the LLM to serve as a collaborative
recommender:

(6) $\displaystyle\mathcal{R}_{\mathit{B}}=f_{\mathit{LLM}}(M_{u}^{s};\
\\{M_{c_{1}},\cdots,\ M_{c_{n}}\\})$

where $M_{u}^{s}$ is the short-term memory of user agent $u$,
$\\{M_{c_{1}},\cdots,M_{c_{n}}\\}$ are the corresponding memories of the
candidate item agents, $n$ is the number of candidates, and $\mathcal{R}$ is
the ranking result.

Advanced Prompting Strategies. Although short-term memory describes the
current preference of a user agent, retrieving their specialized preferences
from long-term memory toward candidates can allow them to make more
personalized inferences. In addition, when interaction records are sparse and
preference propagation is limited, we can further incorporate user historical
interactions into prompts, enabling LLMs to serve as sequential recommenders.
These two improvement strategies can be denoted as follows:

(7) $\displaystyle\mathcal{R}_{\mathit{B+R}}$
$\displaystyle=f_{\mathit{LLM}}(M_{u}^{r};\ M_{u}^{s};\ \\{M_{c_{1}},\dots,\
M_{c_{n}}\\})$ (8) $\displaystyle\mathcal{R}_{\mathit{B+H}}$
$\displaystyle=f_{\mathit{LLM}}(M_{u}^{s};\ \\{M_{i_{1}},\dots,\
M_{i_{m}}\\};\ \\{M_{c_{1}},\dots,\ M_{c_{n}}\\})$

where $M_{u}^{r}$ is the retrieved specialized preference from the long-term
memory of user agent $u$ by taking the memories of candidate items as queries,
and $\\{M_{i_{1}},\cdots,M_{i_{m}}\\}$ are the corresponding memories of the
$m$ historical interactions with items $\\{i_{1},\dots,i_{m}\\}$.

Based on the optimized user agents and item agents, our method can better
simulate diverse interaction behaviors observed in the real world, such as
user-item interactions, users’ social behaviors, and even collective behaviors
within recommender systems. Moreover, since we activate items as agents, it
enables us to explore the potential feasibility of “inventing” more novel and
fascinating interactions among inanimate objects. For example, item-to-item
agent interaction can be useful in item cold-start scenarios by spontaneously
propagating collected user preference to new items (See Section 3.4.2 for in-
depth analysis experiments).

##  3\. Experiments

###  3.1. Experimental Setup

####  3.1.1. Datasets

Following previous work (Hou et al., 2022, 2023a), we conduct experiments on
two text-intensive subsets of Amazon review dataset (Ni et al., 2019): “CDs
and Vinyl” and “Office Products”. Due to expensive API calls, we have to
further sample subsets from these two datasets. Specifically, considering the
effect of data sparsity on collaborative filtering recommenders, we randomly
sample two subsets (one dense and one sparse) from each dataset, with each
subset containing 100 users, exploring more diverse interaction scenarios. The
statistics of the datasets are summarized in Table 1. Note that we use the
dense datasets for further analysis experiments, as they better demonstrate
the interactions among agents.

####  3.1.2. Evaluation Metrics

To evaluate the performance, we take NDCG@K as a metric, where K is set to 1,
5 and 10. Following existing studies (Zhou et al., 2020; Hou et al., 2022), we
employ the leave-one-out strategy for evaluation. Specifically, we consider
the last item in each historical interaction sequence as the ground-truth
item. By adopting the model as a ranker, we rank the target item alongside
nine randomly sampled items. To further reduce randomness, we conduct three
repetitions of all test instances and report the average results.

####  3.1.3. Baseline Models

We compare the proposed method with the following baseline methods:

$\bullet$ BPR (Rendle et al., 2009) leverages matrix factorization to learn
the representations of users and items by optimizing the BPR loss.

$\bullet$ SASRec (Kang and McAuley, 2018) captures the sequential patterns of
user historical interactions by utilizing a transformer-encoder.

$\bullet$ Pop ranks candidates based on their popularity, which is measured by
the number of interactions.

$\bullet$ BM25 (Robertson and Zaragoza, 2009) ranks candidates according to
their textual similarity with user historical interactions.

$\bullet$ LLMRank (Hou et al., 2023b) uses the ChatGPT as a zero-shot ranker,
considering user sequential interaction histories as conditions.

For our approach, we consider three major variants:
$\text{AgentCF}_{\mathit{B}}$, $\text{AgentCF}_{\mathit{B+R}}$, and
$\text{AgentCF}_{\mathit{B+H}}$ (corresponding to Equation (6), (7), and (8),
respectively). As our simulated user and item agents are optimized on sampled
datasets, we compare our method with BPR and SASRec by training them on the
sampled datasets, referred to as $\text{BPR}_{\text{sample}}$ and
$\text{SASRec}_{\text{sample}}$. We also report the performance of these two
models when trained on the complete dataset for reference, denoted as
$\text{BPR}_{\text{full}}$ and $\text{SASRec}_{\text{full}}$. The Pop model,
which relies on statistics to make recommendations, is trained on the complete
datasets. Since LLMRank and BM25 are zero-shot models, training them on the
complete dataset would be meaningless. Therefore, we directly evaluate their
performance on the sampled datasets. Scaling agents for larger datasets is
left as future work.

Table 1. Statistics of the preprocessed datasets. “Avg.c” is the average
number of words in initialized item description text.

Datasets | #Users | #Items | #Inters. | Sparsity | Avg.c
---|---|---|---|---|---
CDs (full) | 93,653 | 64,032 | 1,178,439 | 99.98% | 8.04
–Sparse | 100 | 704 | 800 | 98.86% | 7.76
–Dense | 100 | 269 | 800 | 97.03% | 8.47
Office (full) | 86,530 | 25,842 | 675,683 | 99.97% | 25.14
–Sparse | 100 | 561 | 600 | 98.93% | 25.06
–Dense | 100 | 188 | 600 | 96.81% | 25.48

Table 2. Performance comparison of different models. We highlight the best and
the second-best among traditional recommenders trained on sampled datasets,
tuning-free models, and our approach, using bold and underlined fonts,
respectively.

Method |  CDs${}_{\text{sparse}}$ |  CDs${}_{\text{dense}}$ |  Office${}_{\text{sparse}}$ |  Office${}_{\text{dense}}$
---|---|---|---|---
N@1 | N@5 | N@10 | N@1 | N@5 | N@10 | N@1 | N@5 | N@10 | N@1 | N@5 | N@10
BPR${}_{\text{full}}$ | 0.1900 | 0.4902 | 0.5619 | 0.3900 | 0.6784 | 0.7089 | 0.1600 | 0.3548 | 0.4983 | 0.5600 | 0.7218 | 0.7625
SASRec${}_{\text{full}}$ | 0.3300 | 0.5680 | 0.6381 | 0.5800 | 0.7618 | 0.7925 | 0.2500 | 0.4106 | 0.5467 | 0.4700 | 0.6226 | 0.6959
BPR${}_{\text{sample}}$ | 0.1300 | 0.3597 | 0.4907 | 0.1300 | 0.3485 | 0.4812 | 0.0100 | 0.2709 | 0.4118 | 0.1200 | 0.2705 | 0.4576
SASRec${}_{\text{sample}}$ | 0.1900 | 0.3948 | 0.5308 | 0.1300 | 0.3151 | 0.4676 | 0.0700 | 0.2775 | 0.4437 | 0.3600 | 0.5027 | 0.6137
Pop | 0.1100 | 0.2802 | 0.4562 | 0.0400 | 0.1504 | 0.3743 | 0.1100 | 0.2553 | 0.4413 | 0.0700 | 0.2273 | 0.4137
BM25 | 0.0800 | 0.3066 | 0.4584 | 0.0600 | 0.2624 | 0.4325 | 0.1200 | 0.2915 | 0.4693 | 0.0600 | 0.3357 | 0.4540
LLMRank | 0.1367 | 0.3109 | 0.4715 | 0.1333 | 0.3689 | 0.4946 | 0.1750 | 0.3340 | 0.4728 | 0.2067 | 0.3881 | 0.4928
AgentCFB | 0.1900 | 0.3466 | 0.5019 | 0.2067 | 0.4078 | 0.5328 | 0.1650 | 0.3359 | 0.4781 | 0.2067 | 0.4217 | 0.5335
AgentCFB+R | 0.2300 | 0.4373 | 0.5403 | 0.2333 | 0.4142 | 0.5405 | 0.1900 | 0.3589 | 0.5062 | 0.1933 | 0.3916 | 0.5247
AgentCFB+H | 0.1500 | 0.4004 | 0.5115 | 0.2100 | 0.4164 | 0.5198 | 0.2133 | 0.4379 | 0.5076 | 0.1600 | 0.3986 | 0.5147

###  3.2. Overall Performance

We compare our approach with the baseline methods on four datasets and present
the results in Table 2. We can find:

(1) Our approach outperforms other baselines in most scenarios, highlighting
the effectiveness of collaborative learning for simulating personalized
agents. Specifically, the performance achieved by adopting our model as a
collaborative filtering recommender (AgentCFB) demonstrates the efficacy of
preference propagation in modeling the collaborative filtering idea. In
addition, retrieving user agents’ specialized preferences toward candidates
(AgentCFB+R) enables more personalized inferences. In sparse interaction cases
(Office dataset), incorporating user historical interactions to enhance the
prompt and enabling LLMs to serve as a sequential recommender (AgentCFB+H) can
make better performance.

(2) Our model exhibits superior or comparable performance to traditional
recommendation models when trained on datasets of the same scale (sampled
datasets). Furthermore, our model even achieves comparable performance to
traditional recommenders trained on full datasets, when dealing with sparse
scenarios. Although our model still has room for improvement in other
scenarios, it is worth emphasizing that our model is trained using only
approximately 0.07% of the complete dataset. These results further demonstrate
the generalization capability of our approach.

(3) Existing tuning-free methods, such as Pop, BM25, and LLMRank, exhibit
unsatisfactory performance. Specifically, although LLMRank employs ChatGPT to
model user preferences by introducing user historical interactions as prompts,
similar to conclusions in (Hou et al., 2023b; Liu et al., 2023), its
performance falls significantly behind that of traditional models. This
highlights the gap between user behavior patterns and universal knowledge of
LLMs, which hinders the effectiveness of LLMs in capturing user preferences
from their behaviors.

###  3.3. Further Model Analyses

Table 3. Ablation study on two sampled datasets.

Variants |  CDs${}_{\text{dense}}$ |  Office${}_{\text{dense}}$
---|---|---
N@1 | N@10 | N@1 | N@10
AgentCFB | 0.2067 | 0.5328 | 0.2067 | 0.5335
$w/o$ Auto. Interaction | 0.1200 | 0.4964 | 0.1733 | 0.5031
$w/o$ User Agent | 0.1100 | 0.4693 | 0.2200 | 0.5145
$w/o$ Item Agent | 0.1767 | 0.5128 | 0.1800 | 0.5169

####  3.3.1. Ablation Study

Our proposed agent-based collaborative filtering approach contains the agents’
autonomous interaction part, the optimization of user agents, and the
optimization of item agents. The details of prompts are presented in Appendix
C. To verify the effectiveness of each component, we conduct the ablation
study on two dense datasets and record the results in Table 3.

(1) $w/o$ Autonomous Interaction: In this scenario, we directly provide real
user interaction records to agents, and prompt them to explore the reasons
behind real user’s decisions. The performance gap reveals that comparing
agents’ autonomous interactions with real-world interaction records can
generate comprehensive feedback about the misalignment between simulated
agents and real-world individuals, which enhances the efficacy of reflection.

(2) $w/o$ User Agent: To remove user agent optimization, we directly represent
each user with their historical interactions, without any memory update. The
performance decline indicates that the simple verbalized text of user
interaction behavior can not reflect user underlying preferences, emphasizing
the efficacy of adjusting agents’ personalized memories by enabling them to
autonomously simulate real user behaviors. Although prompting LLMs with user
historical interactions can yield better results in the Office dataset, we
argue that this is due to the dataset’s longer item description text, allowing
LLMs to effectively serve as sequential recommenders. However, even in this
case, stimulating LLM-powered item agents can lead to improved performance,
further demonstrating the significance of facilitating items to capture
preferences of adopters.

(3) $w/o$ Item Agent: Not optimizing item agents (_i.e.,_ representing items
with corresponding identity information) also leads to worse performance,
highlighting the effectiveness of behavior-involved item-side modeling in
capturing two-sided interaction relations between users and items. Notably,
item agents play a crucial role in simulating collaborative filtering
recommender systems, by updating their memory with user preference information
and propagating this information to new agents through interactions.

font=large

####  3.3.2. Performance Copmarison w.r.t. Position Bias and Popularity Bias

In this part, we evaluate our approach’s ability to simulate personalized
agents, by obliquely examining whether they are susceptible to position and
popularity bias in ranking candidates, as general LLMs heavily rely on such
common-sense knowledge to make judgments. The results in Figure
LABEL:fig:pos_pop_bias show that an LLM prompted by user historical
interactions (LLMRank) is influenced by both popularity bias and position
bias. It tends to select popular items and those positioned higher. In
contrast, our approach, although inevitably affected by these biases, performs
enhanced stability. This confirms that our approach simulates personalized
user agents, enabling them to rank candidates based on their personalized
preferences rather than relying solely on general knowledge.

####  3.3.3. Effectiveness of Collaborative Reflection

To explore the efficacy of collaborative reflection in agent optimization, we
evaluate the change in the alignment between agents and real users as
optimization progresses. In this experiment, we extract the last three
interactions from user behavior sequences to optimize agents, which we refer
to as three optimization steps. At each step of optimization, we test whether
these agents can select the positive item from two candidates before and after
optimization. As illustrated in Figure LABEL:fig:reflection, continuous
optimization enables user agents to align their preferences with real users,
leading to an increasing number making correct choices on initial attempts
(_i.e.,_ before optimizing at this step). Moreover, around 95% of user agents
can make correct choices after collaborative reflection, highlighting its
effectiveness.

###  3.4. Simulations on Other Types of Interactions

####  3.4.1. User-user Interaction Simulation

Real-world users often seek advice from others when interacting with
unfamiliar items. For example, they may browse reviews on shopping websites or
consult with friends for information. To explore whether user agents can
exhibit similar social behaviors, we simulate user-user interaction via
AgentCF, where user agents read and write reviews. Specifically, given the
test items that test users haven’t interacted with, we first ask other user
agents, who have previously interacted with these items, to write reviews. The
test users are then prompted to read these reviews and make decisions. We
compare the decisions of the test users before and after viewing these reviews
and present the results in Figure LABEL:fig:uutest. As we can see, the
simulated user agents exhibit behaviors akin to those of real users. They show
a growing inclination to purchase items upon encountering positive reviews,
refrain from purchasing after encountering negative reviews, and trust reviews
from users with similar preferences. We present the user agents’ explanations
for attitude changes after viewing reviews, as well as their discussions about
items in Appendix C.2.

####  3.4.2. Item-item Interaction Simulation

Recommender systems face challenges in suggesting new items. In this part, we
aim to alleviate the cold-start problem by exploring autonomous interactions
between new and popular item agents. Specifically, we prompt these new item
agents, equipped with identity information such as titles and categories, to
interact with and learn from popular item agents that have extensive
interaction records. This helps warm up cold-start item agents by estimating
adopter preferences and adjusting their memory. Then we prompt user agents to
rank the new items among nine other randomly sampled but well-trained items,
and compare the ranking results obtained using cold-start memory and adjusted
memory. As illustrated in Figure LABEL:fig:iitest, the interaction process
enhances the propagation of collected user preferences to new items and
improves ranking performance. Notably, we find that even interactions with
agents that differ in identity information can also alleviate the cold-start
problem to some extent. This implies that the new item agents do not simply
replicate other agents’s memories, but rather comprehend the relationship
between identity information and personalized memory. It indicates that item-
item interactions activated by AgentCF benefit recommender systems.

####  3.4.3. Process of Preference Propagation

As previously mentioned, our approach achieves preference propagation via
collaborative optimization. Here we illustrate this process in detail.
Specifically, we first initialize the memory of a seed user agent with special
preference descriptions that do not spontaneously emerge during normal
optimization. The user and item agents are then prompted to autonomously
interact and optimize their memories. We showcase the optimized memories of
several agents in Figure 5. As we can see, through collaborative optimization,
user and item agents mutually interact, acting as bridges that facilitate the
propagation of their preferences to other agents with similar interaction
behaviors. To assess preference propagation efficiency, we ask each user agent
about their possession of the seed user’s preferences. The results are
presented in Figure LABEL:fig:information_diffusion. We observe that through
continuous interactions, an increasing number of user agents with similar
behaviors to the seed user can express similar preferences. Additionally, as
the interaction scope expands, the proportion of user agents expressing such
preference gradually decreases. This indicates that information decay during
the propagation process, in line with real-world information diffusion
principles (Yang and Leskovec, 2010). These results demonstrate the potential
of our method in developing collaborative filtering systems as a cradle for
potential collective intelligence.

Figure 5. Case Study of preference propagation. The preferences of user and
item agents are respectively propagated to other agents with similar behaviors
through interactions.

####  3.4.4. Collaborative Advertisements Creation

Figure 7. A case about user distinct attitudes towards advertisements
generated by an individual agent versus advertisements optimized through
multi-agent debate.

Existing work indicates that cooperation among multiple LLMs can improve the
results generated by individual LLM (Wang et al., 2023d; Liang et al., 2023a).
This is a manifestation of collective intelligence. To explore its potential
in recommender systems, we simulate a typical scenario: the creation of
advertisements. Specifically, we simulate several advertiser agents proficient
in different aspects of advertising, such as personalization, creativity, and
attractiveness. Given an advertisement draft generated by individual LLM, we
prompt these agents to critique and offer suggestions, aiming to generate
higher-quality advertisements. As illustrated in Figure 7, through the
collaboration among these agents, our approach utilizes the strengths of each
aspect and generates more appealing advertisements for users.

##  4\. Related Work

LLM-powered Agents. Recently, LLM-powered agents have shown the potential to
develop Artificial General Intelligence (AGI) due to their capabilities in
reasoning and planning (Zhao et al., 2023; Wang et al., 2023c). Equipped with
memory (Tu et al., 2023; Liang et al., 2023b) and reflection (Shinn et al.,
2023; Miao et al., 2023) modules, these agents can store their past
experiences and make better decisions for future behaviors. Numerous studies
have been proposed to employ agents for simulating human-like interactions,
including daily lives in smallville (Park et al., [n. d.]), debate (Liang et
al., 2023a; Chan et al., 2023; Du et al., 2023), and even game-playing (Wang
et al., 2023e; Gong et al., 2023; Chen et al., 2023). These studies inspire us
to explore the application of LLM-powred agents in simulating user-item
interactions.

Language Model For Recommendation. Numerous attempts have been made to
leverage language models for solving recommendation tasks (Wu et al., 2023b;
Lin et al., 2023a; Fan et al., 2023; Li et al., 2023b). Specifically, these
studies mainly prompt language models to infer user preferences based on user
historical interactions (Gao et al., 2023b; Kang et al., 2023; Zhang et al.,
2023a). However, due to the gap between universal knowledge of LLMs and
domains-specific user behavior patterns, they may fall short in providing
personalized recommendations (Huang et al., 2023; Zhang et al., 2023b). To
solve this, several studies propose to incorporate knowledge of LLMs to
enhance recommendation models, rather than taking LLMs as recommenders (Xi et
al., 2023; Wang et al., 2023a). Some work also specializes LLMs for
recommendation by fine-tuning them on recommendation data, which can be time-
consuming (Geng et al., 2022; Cui et al., 2022; Bao et al., 2023b; Li et al.,
2023c; Lin et al., 2023b; Chen, 2023; Wu et al., 2023a; Zheng et al., 2023;
Bao et al., 2023a). Recently, researchers attempt to incorporate agents into
recommender systems, focusing on facilitating recommendations or user behavior
simulations (Wang et al., 2023f, b; Huang et al., 2023; Ie et al., 2019; Jin
et al., 2023). However, these studies mainly focus on user behavior,
neglecting the modeling of user-item relations, which is the core of
recommender systems. Unlike these methods, we further regard items as agents
and propose the agent-based collaborative filtering approach, enabling user
and item agents to model their two-sided relations.

##  5\. Conclusion and Future Work

In this paper, we proposed _AgentCF_ , an agent-based collaborative filtering
approach for simulating user-item interactions in recommender systems. We
creatively consider not only users but also items as agents, allowing for the
modeling of their two-sided relations through interactions of LLM-powered
agents. Specifically, we develop a collaborative learning approach that
optimizes both kinds of agents together, where these agents perform autonomous
interactions and reflect on the disparities between their decisions and real-
world interaction records. During this process, user and item agents mutually
align their preferences, and propagate this information to other agents in
subsequent interactions, implicitly modeling the collaborative filtering idea.
The simulated user and item agents exhibit human-like behaviors in various
types of interactions (user-item, user-user, item-item, and collective
interactions), demonstrating the effectiveness of AgentCF.

In the future, we will explore more types of real-world scenarios and their
corresponding interactions. In addition, AgentCF is a small step to simulate
not only humans but also inanimate objects with LLM-based agents. It holds the
promise of enhancing inanimate objects with autonomy and emerging intellectual
agent ecosystems that connect everything, which will be studied in our future
work.

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##  Appendix A Implementation Details

We implement AgentCF and other baselines based on a popular open-source
recommendation framework RecBole (Xu et al., 2022; Zhao et al., 2021). We
implement LLMRank using gpt-3.5-turbo-16k-0613. The autonomous item selection
component of AgentCF is implemented using text-davinci-003, while the
collaborative reflection and inference components of AgentCF are implemented
using gpt-3.5-turbo-16k-0613. The hyperparameter temperature, used when
calling APIs of LLMs, is set to 0.

##  Appendix B Training Details

In this part, we present the details and insights of collaborative agent
optimization, with the aim of assisting readers in developing a clearer
comprehension of our approach.

###  B.1. Training Data Format

We employ real users’ historical interaction records as training data to
optimize the agents and update their memory. Specifically, we arrange the real
users’ historical behavioral sequences in chronological order. At each step of
optimization, we take the item from the real user’s behavioral sequences at
this step as a positive item, which is then paired with a negative item for
the user agent to choose from. Consequently, throughout this process, the
agent autonomously simulates the real users’ decision processes, thereby
facilitating their alignment with real-world individuals. We can also view
this as the simulated agent continually evolving through “re-enacting” the
historical interactions of real users.

###  B.2. Candidate Selection Process

We intentionally introduce popular bias and position bias when selecting
candidate negative items. The reason is that we tend to increase the
discrimination difficulty for LLM-powered agents. By doing so, user agents are
more inclined to select negative items (see our experiment in Section 3.3.3
and 3.3.2). This can enable user agents to make more comprehensive
reflections. To achieve this, for popular bias, we compute the popularity of
each item based on its frequency in the interaction records of all users. Then
we convert the popularity into probabilities for sampling, ensuring that items
with higher popularity are more likely to be sampled as negatives. As for
position bias, we place the negative item at the first position in the
candidate list when presenting the candidates to user agents.

###  B.3. Memory Module

Throughout optimization, we prompt the agents to reflect on and adjust any
misleading conceptions in their short-term memories. It is important to
highlight that, during this process, we do not retrieve the user agent’s long-
term preferences from their long-term memory. The reason is that we expect
that user agents could explore more diverse preferences of real users through
collaborative reflection, rather than make more inconsistent decisions with
those of real-world users at the “training stage”.

We append the user agents’ previous short-term memory to their long-term
memory at each optimization step (_i.e.,_ each time step of real user
historical interactions). Therefore, after optimization, the length of the
agent’s long-term memory is equivalent to the length of real user historical
interactions. It’s important to note that due to the relatively short length
of the user’s historical interactions that we currently simulate (an average
of 7 interactions), we can store all their past experiences in the long-term
memory module as a list. However, in more realistic systems with hundreds or
thousands of interaction records, preserving all past experiences may not be
practical. To address this, we can explore options such as using LLM summaries
or compressing past experiences in our future work.

###  B.4. Prompt Disign

As we will present in Appendix C.1, the prompt of the collaborative reflection
process consists of three main components: (1) Reflecting on the reasons
behind inconsistent decisions compared to real users. (2) Exploring new
preferences and dislikes based on the features of positive and negative items.
(3) Removing outdated or inconsistent content from their previous short-term
memory. By integrating these components, the user agents can be prompted to
correct the misleading conceptions in their memories and enhance their
alignment with real users. The user and item agents can also better understand
their two-sided relations by discriminating between positive instances and
negative instances. At each step of interaction, the autonomous item selection
process and the collaborative reflection are iterated until user agents make
consistent choices with real-world interaction records or reach the maximum
round of iterations. The maximum rounds of optimization iterations per step is
set to 2.

If a user agent makes the right choice that aligns with the real behavior, it
indicates that the simulated user agent and item agents have captured their
two-sided relations and exhibit a strong alignment with real-world individuals
(at least in this optimization step). In such cases, we inform them about the
correctness and store the related interaction information in their memory. The
prompt in these instances consists of two main components: (1) Exploring new
preferences and dislikes based on the features of positive and negative items.
(2) Removing outdated or inconsistent content from their previous short-term
memory. In other words, we no longer prompt agents to conduct reflections.

###  B.5. Computational Cost and Efficiency

Due to the high cost of calling LLM APIs and the current inefficiencies in
communication among LLM-powered agents, in this work, we only sample 100 users
and their historical interaction records for each dataset to simulate
interactions between agents.

To further mitigate computational expenses and enhance efficiency, we conduct
several exploratory approaches: (1) We attempt to employ different types of
LLMs at distinct stages of optimization, according to the complexity of the
task to be solved. For instance, when prompting user agents to choose suitable
items from a pair of candidates for interaction (_i.e.,_ the forward process
in collaborative optimization), as this task is relatively straightforward, we
employ “text-davinci-003”, which supports parallel calls. Conversely, when
prompting agents to reflect on the misleading conceptions in their memories or
make inferences, we select the more powerful “gpt-3.5-turbo-16k-0613” to solve
this complex task. (2) We attempt to merge different steps of collaborative
optimization. For example, as illustrated in our prompt C.1, when prompting
the agents to reflect on and adjust their memories, we incorporate
“reflection” and “memory updation” into a unified step for more efficient and
cost-effective optimization. While we find that this integration approach does
not lead to a drop in performance compared to separating these two processes,
one can also separate them, generating textual reflections and offering clear
explanations of the optimization algorithm’s operating status.

Our future work will focus on how to further mitigate computational costs and
facilitate more efficient interaction among agents, with the objective of
scaling agents for simulating larger datasets.

##  Appendix C Prompts and Respones

###  C.1. Agent-based Collaborative Filtering

Initialization
---
$\bullet$ User Agent
I enjoy listening to CDs very much.
$\bullet$ Positive Item Agent
The CD is called “Brainwashed”. The category of this CD is: “Classic Rock;
Album-Oriented Rock (AOR)”.
$\bullet$ Negative Item Agent
The CD is called “O, Yeah! Ultimate Aerosmith Hits”. The category of this CD
is: “Classic Rock; Album-Oriented Rock (AOR)”.
“Forward” Autonomous Interaction
$\bullet$ User Agent${}_{\text{system prompt}}$
You are a CD enthusiast. Here is your self-introduction, expressing your
preferences and dislikes: “$user agent memory”. Now, you are considering to
select a CD from two candidate CDs. The features of these two candidate CDs
are listed as follows: “1. $neg item agent memory \n 2. $pos item memory”. \n
\n Please select the CD that aligns best with your preferences. Furthermore,
you must articulate why you’ve chosen that particular CD while rejecting the
other. \n To do this, please follow these steps:\n 1. Extract your preferences
and dislikes from your self-introduction.\n 2. Evaluate the two candidate CDs
in light of your preferences and dislikes. Make your choice by considering the
correlation between your preferences/dislikes and the features of the CDs.\n
3. Explain why you make such choices, from the perspective of the relationship
between your preferences/dislikes and the features of these candidate CDs. \n
\n Important note:\n 1. Your output should in the format: Chosen CD: [Title of
the selected CD] \n Explanation: [Detailed rationale behind your choice and
reasons for rejecting the other CD].\n 2. When identifying your likes and
dislikes, don’t fabricate them! If your self-introduction doesn’t specify any
relevant preferences or dislikes, use common knowledge to inform your
decision, such as the popularity of the CDs. \n 3. you must choose one of
these two candidates, instead of choosing both of them. \n 4. Your explanation
needs to be comprehensive and specific. A general preference, such as a
certain genre of CD, is insufficient. Your reasoning should delve into the
finer attributes of the CD.\n 5. Base your explanation on facts. For instance,
if your self-introduction doesn’t reveal any specific preferences, you cannot
assert that your decision was influenced by such preferences.”
$\bullet$ User Agent${}_{\text{response}}$
Chosen CD: O, Yeah! Ultimate Aerosmith Hits \n Explanation: I chose O, Yeah!
Ultimate Aerosmith Hits because it is a classic rock album from the Album-
Oriented Rock (AOR) genre, which is a genre I enjoy listening to. The album
contains some of Aerosmith’s greatest hits, which I believe will provide me
with a great listening experience. In comparison, Brainwashed is also a
classic rock album from the AOR genre, but it is not as popular as O, Yeah!
Ultimate Aerosmith Hits, and therefore I believe it will not provide me with
the same level of listening experience.
“Backward” Collaborative Memory-based Optimization
$\bullet$ User Agent${}_{\text{system prompt}}$
You are a CD enthusiast. \n Here is your previous self-introduction,
exhibiting your past preferences and dislikes:“$user agent memory”. Recently,
you considered to choose one CD from two candidates. The features of these two
candidate CDs are listed as follows:“1. $neg item agent memory \n 2. $pos item
memory” . \n \n After comparing these two candidates based on your preferences
and dislikes, you selected “$neg item title” to listen to while rejected the
other one. You provided the following explanations for your choice, revealing
your previous judgment about your preferences and dislikes for these two CDs:
“$user explanation”. However, upon actually listening to these two CDs, you
discovered that you don’t like the CD that you initially chose (Here And There
Remastered). Instead, you prefer the CD that you did not choose before
(Thriller).\n This indicates that you made an incorrect choice, and your
judgment about your preferences and dislikes, as recorded in your explanation,
was mistaken. It’s possible that your preferences and dislikes, described in
your previous self-introduction, have either changed or were uninformative.
Therefore, your task now is to update your self-introduction, by incorporating
your new preferences and dislikes revealed in this interaction. To do this,
please follow these steps: 1. Analyze the misconceptions in your previous
judgment about your preferences and dislikes, as recorded in your explanation,
and correct these mistakes \n 2. Explore your new preferences based on the
features of CDs you really enjoy (Thriller), and determine your dislikes based
on the features of CDs you truly don’t enjoy (Here And There Remastered). \n …
Continued on next column
$\bullet$ User Agent${}_{\text{system prompt}}$
---
… 3. Summarize your past preferences and dislikes from your previous self-
introduction. Combine your newfound preferences and dislikes with your past
preferences and dislikes. Filter and Remove any conflicting or repetitive
parts in your past descriptions that contradict your current preferences and
dislikes. \n 4. Update your self-introduction. Please start by describing your
newfound preferences reflected in this interaction. Then describe your past
preferences summarized and filtered from your previous self-introduction.
Afterward, please describe your dislikes. \n \n Important note:\n 1. Your
output should use the following format: “My updated self-introduction: [Please
write your updated self-introduction here].” \n 2. Keep your updated self-
introduction under 180 words. \n 3. Any overall assessments or summarization
in your self-introduction are forbidden.\n 4. You updated self-introduction
only need to describe the features of CDs you prefer or dislike, without
mentioning your wrong choice or your thinking process in updating your self-
introduction. \n 5. You self-introduction should be specific and personalized.
Any preferences and dislikes that cannot distinguish you from others are not
worth recording.
$\bullet$ User Agent${}_{\text{response}}$
My updated self-introduction: I enjoy listening to CDs that fall under the
classic rock and album-oriented rock (AOR) genres. I particularly appreciate
CDs that offer a unique and distinct sound within these genres. I find myself
drawn to CDs that showcase a variety of hits and popular songs, as they
provide an enjoyable listening experience. Additionally, I have discovered a
newfound preference for CDs that have a more experimental and innovative
approach to classic rock. These CDs offer a refreshing twist on the genre and
keep me engaged throughout. On the other hand, I tend to dislike CDs that lack
originality and fail to bring something new to the table. CDs that rely
heavily on generic rock sounds and do not offer any standout tracks or moments
tend to leave me uninterested. Overall, I seek CDs that captivate me with
their creativity and deliver a memorable listening experience.
$\bullet$ Pos Item Agent${}_{\text{system prompt}}$
Here is the self-introduction of a user, exhibiting his preferences and
dislikes: “$user agent’s memory”.\n Recently, the user browsed a shopping
website and considered to choose one CD from two candidate CDs.\nThe
descriptions of the two candidate CDs are as follows:\n “1.$pos item agent’s
memory \n 2. $neg item agent’s memory ” \n\n By considering his preferences
and dislikes from his self-introduction, he chose “$neg item title” to listen
to while rejected the other one. He provided the following explanations for
his choices: “$user explanation”. \n \nHowever, the user found that he
actually doesn’t enjoy the CD that he previously chosen. Conversely, he finds
the CD that he didn’t choose before, titled “$pos item title”, more appealing.
This indicates that he made an unsuitable choice. This may be because the user
was misled by the descriptions of these two candidate CDs. Actually, the user
prefers a CD (“$pos item title”) because its features aligns with his
preferences and go against his dislikes, while he dislikes the other CD (“$neg
item title”) because it possesses features that he disliked. Therefore, your
task is to update the descriptions of these two candidate CDs. \n To do this,
follow these steps:\n 1. Analyze which features recorded in the previous CD
description led to the unsuitable choice based on the user’s explanation, and
modify them accordingly. \n 2. Analyze the user’s preferences and dislikes
from his self-introduction. Explore the user preferred CD’s new features that
aligns with the user’s preferences and goes against with his dislikes. Explore
the user disliked CD’s new features that aligns with the user’s dislikes and
goes against with his preferences. In this case, the differences between the
descriptions of these two items should be amplified. Your exploration should
be thorough and detailed, rather than just superficial. For example, you can’t
simply say a CD’s genre aligns with or goes against the user’s preferences, as
that is too broad.\n 3. Incorporate the item’s newfound features with the into
its previous description. Since the previous descriptions of the candidate CDs
are based on evaluations from other users, it is also worth preserving
valuable content. Please try to maintain conciseness while preserving the
information as much as possible.\n\n Important notes: \n 1. Your output should
in the following format: “The updated description of the first CD is: [updated
description]. \n The updated description of the second CD is: [updated
description].”. \n 2. Each updated CD description can not exceed 50 words!!!
\n 3. In your updated CD description, considering that it will be visible to
other users, it is advisable to utilize user preferences or dislikes when
referring to a group of users collectively, rather than using “the user” to
specifically denote an individual. For instance, you can mention “the user
with … preferences/dislikes”.\n 4. The updated CD description should not
contradict the inherent characteristics of this CD. For example, you can not
say that a hip-hop CD has a very soothing rhythm. \n 5. The updated CD
description should be specific, containing only the distinguishing features
that differentiate this CD from others.
Continued on next page

$\bullet$ Pos Item Agent${}_{\text{response}}$
---
“Brainwashed” is a classic rock album from the Album-Oriented Rock (AOR) genre
that breaks new ground with its experimental and innovative approach. This CD
offers a refreshing twist on classic rock, delivering a captivating listening
experience that keeps you engaged from start to finish. “Brainwashed” stands
out from generic rock sounds, providing standout tracks and moments that
showcase its originality and creativity. For the user who seeks CDs that push
boundaries and bring something new to the table, “Brainwashed” is a must-
listen.

###  C.2. User-user Interaction

####  C.2.1. Users Reading and Writing reviews

In this experiment, we evaluate whether user agents can enhance their
comprehension of items they have not interacted with, by reading reviews from
other user agents regarding these items.

Specifically, to conduct this experiment, we sample 100 test user agents along
with their corresponding ground-truth items from the datasets. We enable the
user agents to simulate real users’ preferences and make personalized
decisions by optimizing them on several of their historical interactions
(before the ground-truth items). Subsequently, for each ground-truth item, we
select five user agents who have interacted with these items and prompt them
to write both positive and negative reviews about these items based on
different prompts. Following this, the test user agents are prompted to view
the reviews and decide whether to purchase the items. Note that we enable
these test user agents to retrieve related reviews written by user agents with
similar or dissimilar preferences to them, so as to explore whether user
agents can exhibit certain patterns akin to those of real users.

The prompts to direct user agents to write positive and negative reviews are
as follows:

  * •

Positive: “ _Recently, you bought a CD. The description of this CD is as
follows ‘$test item agent memory’. After truly listening to this CD, you
realize that you like it, which indicates that its features align with your
preferences and go against your dislikes. Please write a review to describe
your user experience on this CD based on your preferences and dislikes._ ”

  * •

Negative: “ _Recently, you bought a CD. The description of this CD is as
follows ‘$test item agent memory’. After truly listening to this CD, you
realize that you really dislike it, which indicates that its features align
with your dislikes and go against your preferences. Now, you need to write a
review for this CD on the shopping website._ ”

Initialization
---
$\bullet$ Test User Agent
I have a strong preference for progressive rock music due to its unique and
complex sound, as well as its meaningful and thought-provoking lyrics. I enjoy
the intricate and layered instrumentation that is often found in this genre.
On the other hand, I have a dislike for today’s country music, as it tends to
have a repetitive sound and shallow lyrics. I find this genre uninspiring and
prefer to explore other musical styles.
$\bullet$ Test Item Agent
The CD is called “Livonia”. The category of this CD is: “Alternative Rock;
Indie & Lo-Fi; Indie Rock”
Continued on next column

Prompt users who have previously purchased the item to write a review.
---
$\bullet$ User Agent${}_{\text{system prompt to write the review}}$
Here is your self-description, exhibiting your preferences and dislikes:
“$test user agent memory’. Recently, you bought a CD. The description of this
CD is as follows: “$test item agent memory” \n\n After truly listening to this
CD, you realize that you like it, which indicates that its features align with
your preferences and go against your dislikes. Please write a review to
describe your user experience on this CD based on your preferences and
dislikes. \n\n Important note: \n 1. Your review should not exceed 50 words.
\n 2. Your review needs to be objective, as it may be referenced by other
customers.\n 3. Your reviews must not involve fake information. For example,
you cannot claim that a product has features it actually does not possess.
$\bullet$ User Agent${}_{\text{response to write the review}}$
I was pleasantly surprised by the CD ’Livonia’. It is a great blend of
alternative rock, indie, and lo-fi genres, with melodic and meaningful lyrics.
It is not too loud or aggressive, and avoids the dark and depressing lyrics of
metal and pop metal genres. Highly recommended for those who appreciate a
unique and dynamic listening experience.
The test users’ choices before reading the reviews.
$\bullet$ User Agent${}_{\text{system prompt}}$
You are a CD enthusiast. Here is your self-description, exhibiting your
preferences and dislikes: “$test user agent memory’. Now, a recommender system
recommends “$test item title” for you. The characteristics of this CD are as
follows: “$test item agent memory’ \n Please tell us if you like this CD based
on your preference and dislikes and the review you examined. \n Important
note: If you find it difficult to determine whether you like this CD based on
your personal preferences or dislikes, you can also rely on common knowledge
to make a judgment. For instance, you can take into account the popularity of
this CD. \n\n Please output your judgment and explain why you make such
judgment. \n Please generate your output in the following format: “Choice:
[Yes / No] \\\n Explanation: [Why you make such choice]”.
$\bullet$ User Agent${}_{\text{response}}$
Choice: No\n Explanation: Based on my personal preferences and dislikes, I
would not like this CD. Although the CD falls under the alternative rock,
indie, and indie rock categories, which are genres I generally enjoy, the
specific characteristics of this CD are not aligned with my preferences. As a
progressive rock enthusiast, I appreciate the unique and complex sound, as
well as the meaningful and thought-provoking lyrics that are often found in
the genre. The description of “Livonia” does not mention these elements.
The test users’ choices after reading the reviews.
$\bullet$ User Agent${}_{\text{system prompt}}$
To further validate your choice, you decide to explore its obtained reviews.
In doing so, you find some reviews left by users who share similar preferences
to yours.\n These reviews are as follows: “$other user’s review”.\n\n By
analyzing these reviews, you can better judge if this CD aligns with your
taste. In that case, would you change your previous decision? \n Please output
your whether you like this CD and explain why you make such judgment. \n
Please generate your output in the following format: “Choice: [Yes / No. Yes
means you like this CD, and No means you still like this CD] \\\n Explanation:
[Why you make such choice]”.
$\bullet$ User Agent${}_{\text{response}}$
Choice: Yes\n Explanation: After examining the reviews left by users who share
similar preferences to mine, I have decided to change my previous decision.
The review states that ’Livonia’ is a great blend of alternative rock, indie,
and lo-fi genres with melodic and meaningful lyrics. This aligns with my
preference for unique and dynamic music with thoughtful lyrics. The review
also mentions that the CD avoids the dark and depressing lyrics often found in
other genres, which is another positive aspect for me. Considering these
factors, I believe that ’Livonia’ would be a CD that I would enjoy based on my
personal preferences.

####  C.2.2. Users Engaging in Free Discussions about Items

In this part, we facilitate a free discussion between two user agents to share
their viewpoints on the item. In this scenario, user agent A has already
purchased the item, while user agent B, who is seeking advice, has not made a
purchase yet. We first ask user agent A to write a review for this item based
on his preference and experience, serving as his cognition of this item. Then,
these two agents are prompted to discuss the item freely. Through interactions
with friends, user agent B enhances his understanding of the item and
ultimately chooses to make the purchase.

Initialization
---
$\bullet$ User Agent A
I enjoy listening to a wide range of genres, including Pop and Adult
Alternative. I appreciate the diverse sounds and styles within these genres,
from upbeat and catchy to slow and meaningful. The thoughtful and meaningful
lyrics, along with the catchy and memorable melodies, make this genre
particularly appealing to me. I have become more open to different genres and
styles of music, allowing me to make more informed decisions when selecting
music to listen to. On the other hand, I have a dislike for the Country and
Bluegrass genre. I find it often repetitive, with simple and straightforward
lyrics and melodies. The limited instrumentation in this genre does not
resonate with my current preferences. Overall, my updated self-description
reflects my evolving taste in music, with a preference for diverse and
meaningful genres like Pop and Adult Alternative, while disliking repetitive
and simplistic genres like Country and Bluegrass.
$\bullet$ User Agent A Review
I recently bought ’The Band Of Heathens’ CD and I’m pleasantly surprised. It
has a unique blend of Country and Americana, with thoughtful and meaningful
lyrics, catchy melodies, and diverse instrumentation. It goes against my
previous dislike for Country and Bluegrass, as it is far from repetitive and
simplistic. I highly recommend this CD to anyone who enjoys Pop and Adult
Alternative genres.
$\bullet$ User Agent B
I enjoy listening to Pop music because it is upbeat, catchy, and easy to sing
along to. I appreciate the meaningful and relatable lyrics, as well as the
simple and easy-to-follow instrumentals. However, I do not enjoy Dance &
Electronic; Ambient music because it tends to be repetitive and lacks
meaningful lyrics. The complex and difficult-to-follow instrumentals in this
genre are unappealing to me.
$\bullet$ Test Item Agent
The CD is called “The Band Of Heathens”. The category of this CD is: “Country;
Americana”.
Conversation
$\bullet$ User Agent A${}_{\text{system prompt}}$
You are a CD enthusiast. Here is your self-description, exhibiting your
preferences and dislikes: “$user agent A memory”. \n Recently, you bought a
CD. The features of this CD are as follows: “$test item agent memory”. \n\n
After truly listening to this CD, you realize that you really like it, which
indicates that its features align with your preferences and go against your
dislikes. You write a review for this CD on the shopping website, in order to
explain why you enjoy this CD. The review is as follows:\n “$user agent A
explanation”.\n\n Now, one of your friends also wants to buy this CD, and he
wants to consult with you about some information related to the CD and
consider if it’s really suitable for him. Please have a conversation with him.
\n Please note that if you feel your conversation is coming to end, please say
goodbye to your friend.
$\bullet$ User Agent B${}_{\text{system prompt}}$
You are a CD enthusiast. Here is your self-description, exhibiting your
preferences and dislikes: “$user agent B memory”. \n Now, you want to buy a
CD. The features of this CD are as follows: “$test item agent memory ”. \n\n
But you’re currently worried if this CD really matches your taste. Luckily,
you have a friend who has already experienced this CD, and you can consult
with them about whether it’s worth buying and if it aligns with your
preferences. So, please have a conversation with them. \n Please note that if
you feel your conversation is coming to an end, please say goodbye to your
friend and end the chat.
Continued on next column

$\bullet$ User Agent B
---
Hi, bro. I want to buy the CD titled ’The Band Of Heathens’, but I’m not sure
if it’s worth purchasing. I heard you’ve already bought it. What do you think
of it? Is it worth buying?
$\bullet$ User Agent A
Hey! Yeah, I actually bought ’The Band Of Heathens’ CD and I really enjoyed
it. It’s a unique blend of Country and Americana, which might not be
everyone’s cup of tea, but I found it to be really refreshing. The lyrics are
thoughtful and meaningful, and the melodies are catchy. The instrumentation is
also quite diverse, which adds to the overall appeal of the CD. It definitely
goes against my previous dislike for Country and Bluegrass, as it’s far from
repetitive and simplistic. I would highly recommend it, especially if you
enjoy genres like Pop and Adult Alternative.
$\bullet$ User Agent B
Hey, thanks for sharing your thoughts on ’The Band Of Heathens’ CD! It’s great
to hear that you enjoyed it and found it refreshing. I’m glad to know that
it’s not repetitive and simplistic like some other Country and Bluegrass
music. The thoughtful lyrics and catchy melodies definitely sound appealing to
me. It’s interesting that you mentioned it might appeal to fans of Pop and
Adult Alternative, which are genres I enjoy. I think I’ll give it a try based
on your recommendation. Thanks for your input!
$\bullet$ User Agent B
You’re welcome! I’m glad I could help. I think you’ll really enjoy ’The Band
Of Heathens’ CD, especially if you’re a fan of Pop and Adult Alternative
genres. It’s always great to explore new music and expand our horizons. I hope
you have a fantastic time listening to it! Let me know what you think once
you’ve had a chance to give it a listen. Enjoy!

###  C.3. Item-item Interaction

In this experiment, we explore whether interactions between new and popular
item agents can enable new item agents to estimate the preferences of
potential adopters and alleviate cold-start problems. To do this, for well-
trained user and item agents, we first simulate the item cold-start scenario
by removing the memories of ground-truth item agents, and only retaining their
identity information, such as titles and categories. Then, we enable these
“new” item agents to retrieve and interact with several popular item agents
that possess either similar or distinct identity information to them but have
rich interaction records, by performing dense retrieval and taking their
identity information as queries. During this process, the “new” item agents
read the memory of popular item agents, thereby estimating their potential
descriptions and adjusting their memory. We finally prompt the user agents to
rank these ground-truth items among nine other randomly sampled but well-
trained item agents, and compare the ranking results obtained using the
original cold-start memories and the adjusted memories.

Initialization
---
$\bullet$ Cold-start Item Agent
The CD is called “Early Days: The Best of Led Zeppelin, Vol. 1”. The category
of this CD is: “Rock; Rock Guitarists; Guitar Gods”.
$\bullet$ Popular Item Agent A
“Led Zeppelin 1” is a Rock CD that epitomizes captivating rock music with
powerful guitar solos and a raw energy. It showcases exceptional vocal
talents, heartfelt lyrics, beautiful melodies, and soulful performances. This
CD is perfect for those who appreciate the melodic guitar solos and the
overall sound of classic rock and album-oriented rock genres. It aligns with
the preferences of users who enjoy CDs with exceptional vocals, heartfelt
lyrics, beautiful melodies, and soulful performances in the rock, classic
rock, and album-oriented rock genres. With its timeless sound and captivating
energy, “Led Zeppelin 1” is a must-have for rock music enthusiasts looking for
a truly immersive listening experience.’
$\bullet$ Popular Item Agent B
“The Complete Studio Recordings” is a rock album that focuses on guitar gods
and their virtuosity. It offers a wide range of rock music styles, making it
captivating and inspiring for fans of rock and guitar enthusiasts. This CD is
suitable for those who enjoy albums that showcase the skills of rock
guitarists and highlight their virtuosity, while offering a diverse range of
rock music styles to keep the listening experience fresh and exciting.
$\bullet$ Popular Item Agent C
“Remasters” is a must-have for rock enthusiasts who appreciate the skill and
artistry of rock guitarists and guitar gods. This electrifying CD features a
collection of rock songs that highlight the exceptional talent and virtuosity
of these musicians, making it a perfect choice for those seeking a powerful
and captivating rock experience. With its focus on rock guitarists and its
showcase of their exceptional talent, “Remasters” is sure to captivate fans of
the genre.
$\bullet$ Popular Item Agent C
“ZZ Top - Greatest Hits” is a classic rock and album-oriented rock (AOR) CD
that showcases the timeless sound and captivating melodies of these genres. It
offers a compilation of hits from ZZ Top, allowing fans to immerse themselves
in their rich and layered compositions. With its intricate guitar riffs,
bluesy influences, and energetic performances, this CD perfectly aligns with
the preferences of fans of classic rock and AOR, including the user with a
newfound preference for these genres.
Cold-start Item Memory Refinement
$\bullet$ Cold-start Item Agent${}_{\text{system prompt}}$
“Early Days: The Best of Led Zeppelin, Vol. 1” is a captivating rock CD that
showcases the exceptional talent of Led Zeppelin. With powerful guitar solos,
raw energy, and soulful performances, it offers a truly immersive listening
experience for rock music enthusiasts. Perfect for those who appreciate
melodic guitar solos, heartfelt lyrics, and beautiful melodies in the classic
rock and album-oriented rock genres. A must-have for fans seeking a timeless
and captivating rock compilation.

###  C.4. Process of Preference Propagation

In this part, we illustrate the process of preference propagation. We first
initialize the seed user agent’s memory with special preference descriptions.
Then, we explore whether other user agents can exhibit similar preferences.
There are two noteworthy points:

(1) We have conducted a preliminary examination to ensure that the special
preferences initialized for seed users will not be learned during the regular
optimization process.

(2) When we ask user agents whether have such preferences, we emphasize that
their answers must be based on their own preferences, in order to avoid
creating hallucinations. Here is the query prompt: “ _Do you tend to favor
music that evokes emotions and resonates with you? You must answer this
question based on your preferences, rather than relying on your general
knowledge._ ”. We also analyze the memories of user agents for relevant
preferences using the technique of keyword matching, and the results are
similar to those obtained by directly employing prompts for inquiry.

Initialization
---
$\bullet$ Seed User
I tend to favor music that evokes emotions and resonates with me. Whether it’s
the emotions expressed in the lyrics or the emotions conveyed through the
music itself, if a song can resonate with me on an emotional level, I will
develop a deeper love for it.
Interaction between the seed user agent and item agent A
$\bullet$ Memory of Interacted Item Agent A
“Satch Plays Fats - Louis Armstrong Plays The Music Of Fats Waller”. This CD
combines beautiful compositions with powerful vocals, creating a synergy that
captivates listeners on an emotional level. The profound emotions evoked by
the music are timeless, continuing to resonate with listeners even after years
have passed. With a focus on classic vocalists, this CD aligns perfectly with
the user’s preference for music that moves them and touches their heart. It
creates a deep emotional connection that the user values in their music
experience.
Intearction between the user agent B and item agent A
$\bullet$ Memory of Interacted User Agent B
I have a preference for CDs that feature beautiful compositions with strong
melodies, well-crafted arrangements, and powerful vocals that effectively
convey emotions. I value music that creates a deep emotional connection with
listeners, evoking profound emotions that resonate over time. CDs that combine
compositions and vocals in a synergistic way, resulting in a captivating and
emotional experience, are particularly appealing to me. I enjoy genres such as
classic rock and album-oriented rock (AOR) that showcase intricate
instrumentation and memorable melodies. On the other hand, I have a dislike
for CDs categorized under comedy and spoken word, as they do not align with my
preference for music that moves and touches my heart. I prioritize music that
evokes emotions rather than humor or spoken word performances. This analysis
showcases my personalized taste in music, focusing on my preferences for
beautiful compositions, powerful vocals, and deep emotional connections, while
highlighting my dislike for comedy and spoken word CDs.
Intearction between the user agent B and item agent C
$\bullet$ Memory of Interacted Item Agent C
“Sticky Fingers” is a classic rock and album-oriented rock (AOR) CD that
perfectly aligns with the preferences of rock music enthusiasts who appreciate
beautiful compositions, strong melodies, and well-crafted arrangements. This
CD showcases a collection of powerful and expressive vocal performances that
effectively convey the emotions embedded in the lyrics. The compositions and
vocals synergize to create a captivating and emotional experience, evoking
profound emotions that resonate over time. With its genre and musical style,
“Sticky Fingers” offers a deep emotional connection with listeners, making it
a must-have for those who seek music that touches their heart and soul.

###  C.5. Collaborative Advertisements Creation

In this experiment, we simulate a classic scenario: advertisement creation. We
attempt to optimize the advertisement generated by individual agents through
cooperation and debate among multiple agents. Specifically, we first simulate
a CD advertiser (Advertiser Agent N), which will be prompted to generate a
draft advertisement. Then, we simulate three advertiser agents proficient in
different aspects of advertising, such as personalization (Advertiser Agent
P), creativity (Advertiser Agent C), and attractiveness (Advertiser Agent A).
We prompt them to critique and make suggestions for the draft advertisement
generated by the novice advertiser. We further compare users’ evaluations of
draft and refined ads. As illustrated in Figure 7, through the collaboration
among these agents, our approach uses the strengths of each aspect and
generates more appealing advertisements for users.

Initialization
---
$\bullet$ Advertiser Agent N
Your main task is to write an advertisement for the “Yes I Am” CD, which has
the following description. \n \n Important note: Your advertisements must not
involve false advertising, as it is illegal!
$\bullet$ Advertiser Agent P
You excel at crafting ’personalized’ advertisements by contemplating how you,
as an audience member, would react upon seeing them, thereby optimizing the
content of the advertisement.
$\bullet$ Advertiser Agent C
You excel at crafting creative advertisements that captivate audiences through
innovative and distinctive means, leaving a profound impression and standing
out in a fiercely competitive market.
$\bullet$ Advertiser Agent A
You specialize in writing ’attractive’ advertisements that capture the
attention of users through focusing on benefits and using persuasive language.
Draft Advertisement
$\bullet$ Advertiser Agent N${}_{\text{system prompt}}$
Please try your best to write an advertisement for this CD. \n\n Important
note:\n 1. The advertising draft you write should not exceed 150 words.
$\bullet$ Advertiser Agent N${}_{\text{response}}$
Introducing the “Yes I Am” CD, a musical masterpiece that will captivate your
senses and leave you craving for more. Immerse yourself in the soulful
melodies and powerful lyrics that will touch your heart and inspire your soul.
\n Featuring an incredible lineup of talented artists, this CD showcases a
diverse range of genres, from pop and rock to R&B and country. Each track is
carefully crafted to deliver an unforgettable listening experience, with
catchy hooks and infectious beats that will have you dancing and singing
along.\n With its exceptional production quality, the ’Yes I Am’ CD guarantees
a crystal-clear sound that will transport you to another world. Whether you’re
driving in your car, relaxing at home, or hosting a party, this CD is the
perfect companion for any occasion.\n Don’t miss out on the opportunity to own
this extraordinary collection of music. Get your copy of the ’Yes I Am’ CD
today and let the magic of music take you on a journey like never before.
Collaboration: Critique & Suggestions
$\bullet$ Advertiser Agent P/C/A${}_{\text{system prompt}}$
Recently, your company is writing advertisements for “$test item title” CD,
which has the following description: “$test item description”. \n\n Your
colleague has written a draft advertisement for this CD. The specific content
of the draft is as follows: “$draft advertisement’.\n\n However, his ability
in your professional field may not be as strong as yours, so this draft may
not be perfect. Therefore, please provide him with modification suggestions
based on your professional expertise. \n\n Important note:\n 1. Your output
should follow the format below: “My suggested revisions: […]”\n 2. Your
suggestion should not exceed 40 words, therefore your suggestion should be
specific. \n 3. You only need to provide suggestions for modifications,
without directly making the changes for him.
Continued on next column

$\bullet$ Advertiser Agent P${}_{\text{response}}$
---
\- Emphasize the unique qualities of the CD, such as the innovative sound or
the thought-provoking lyrics.\n - Highlight the specific artists featured on
the CD to attract fans of their music.\n- Use more vivid language to describe
the listening experience and the emotions it evokes.\n - Add a call-to-action
to create a sense of urgency and encourage immediate purchase.
$\bullet$ Advertiser Agent C${}_{\text{response}}$
\- Emphasize the unique selling points of the CD, such as the innovative sound
production techniques and the distinctiveness of the artists’ styles.\n- Add
specific examples of the diverse genres represented on the CD to create
intrigue and appeal to a wider audience.\n - Highlight the emotional impact of
the music and how it can resonate with listeners on a personal level.\n -
Consider adding a call to action that encourages readers to take immediate
action and purchase the CD.
$\bullet$ Advertiser Agent A${}_{\text{response}}$
\- Replace “Yes I Am” with a more attention-grabbing title that reflects the
CDś unique qualities. \n - Highlight the CDś standout features, such as the
emotional depth of the lyrics and the exceptional vocal performances. \n -
Emphasize the CDś versatility by mentioning specific occasions where it can be
enjoyed, like road trips, cozy nights in, and lively gatherings.
Refinement
$\bullet$ Advertiser Agent N${}_{\text{system prompt}}$
Your colleagues have provided some revision suggestions for your draft based
on their expertise.\n \n Specifically, their suggestions are listed as
follows:\n “$revise suggestions’. \n\n Please take your colleagues’
suggestions into account and make revisions to your draft.\n Important note:
\n 1. Please generate your revised advertisement directly, without any
additional thinking process. \n 2. Your revised advertisement draft should not
exceed 200 words. \n 3. If you feel that their opinions hold no value, you can
also choose not to heed their advice.\n 4. While considering your colleagues’
suggestions, it is important to stay true to yourself and avoid being misled
by their mistakes.
$\bullet$ Advertiser Agent N${}_{\text{response}}$
Immerse yourself in the raw emotion of heartfelt lyrics and the exceptional
vocal performances that will resonate deep within your soul. \n With an
innovative blend of genres, “Yes I Am” showcases a diverse range of music,
from the infectious beats of pop and rock to the soul-stirring melodies of R&B
and country. Each track is meticulously crafted, utilizing cutting-edge sound
production techniques that will transport you to another dimension.\n Whether
you’re embarking on a road trip, enjoying a cozy night in, or hosting a lively
gathering, “Yes I Am” is the perfect companion for any occasion. Let the music
wash over you, evoking a whirlwind of emotions and connecting you to the very
essence of the human experience.\n Featuring an extraordinary lineup of
renowned artists, including chart-topping sensations and rising stars, “Yes I
Am” is a testament to the power of music. Don’t miss your chance to own this
extraordinary collection. Take action now and embark on your own “Yes I Am”
Get your copy today and let the magic unfold.
Evaluation
$\bullet$ Test User Agent${}_{\text{system prompt}}$
You are a CD enthusiast. Here is your self-introduction, exhibiting your
preferences and dislikes: “$user description”. Now, you are viewing an
advertisement: “$item ads”. \n\n Does this advertisement capture your
attention? \n\n Please generate your output in the following format: “Choice:
[yes / no] \\\n Explanation: [Explanation of why you make such choice]”. \n\n
Important note:\n 1. Please carefully consider each aspect before making your
decision. \n 2. If you answer “yes,” it means you consider this advertisement
to be informative, creative, and aligned with your preferences. Otherwise, you
should respond with “no”.\n 3. Please make deep analysis.
$\bullet$ Test User Agent${}_{\text{response to draft ads}}$
No. While the advertisement does highlight a diverse range of genres,
including pop and rock, which align with my preferences, it lacks specific
details about the artists and tracks featured on the “Yes I Am” CD. Without
this information, it is difficult for me to gauge whether the CD will truly
captivate my senses and leave me craving for more. Additionally, the
advertisement focuses more on the overall experience and production quality
rather than the specific qualities of the music itself, which is a key factor
for me in deciding whether to purchase a CD. Therefore, I am not sufficiently
convinced by this advertisement to consider purchasing the “Yes I Am” CD.
$\bullet$ Test User Agent${}_{\text{response to refined ads}}$
Yes. This advertisement captures my attention because it aligns with my
preferences for CDs that offer a diverse range of genres and exceptional vocal
performances. The mention of heartfelt lyrics and the ability to evoke
emotions resonates with my preference for CDs with meaningful and
introspective lyrics. The promise of an innovative blend of genres, including
pop, rock, R&B, and country, appeals to my love for CDs that showcase a
variety of musical styles. Additionally, the mention of cutting-edge sound
production techniques and the use of renowned artists further piques my
interest in experiencing a vibrant and dynamic listening experience. Overall,
this advertisement seems informative, creative, and aligned with my
preferences, making me inclined to consider purchasing “Yes I AM”.