# Exploring User Retrieval Integration towards Large Language Models for
Cross-Domain Sequential Recommendation

Tingjia Shen<EMAIL_ADDRESS>University of Science and Technology of
ChinaHefeiChina ,  Hao Wang<EMAIL_ADDRESS>University of Science and
Technology of ChinaHefeiChina ,  Jiaqing Zhang<EMAIL_ADDRESS>University of Science and Technology of ChinaHefeiChina ,  Sirui Zhao
<EMAIL_ADDRESS>University of Science and Technology of ChinaHefeiChina
,  Liangyue Li<EMAIL_ADDRESS>China Alibaba
GroupHangzhouChina ,  Zulong Chen<EMAIL_ADDRESS>China Alibaba
GroupHangzhouChina ,  Defu Lian<EMAIL_ADDRESS>University of Science
and Technology of ChinaHefeiChina and  Enhong Chen<EMAIL_ADDRESS>University of Science and Technology of ChinaHefeiChina

(2017)

###### Abstract.

Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users’
sequential preferences across different domains to alleviate the long-standing
cold-start issue. Traditional CDSR models capture collaborative information
through user and item modeling while overlooking valuable semantic
information. Recently, Large Language Model (LLM) has demonstrated powerful
semantic reasoning capabilities, motivating us to introduce them to better
capture semantic information. However, introducing LLMs to CDSR is non-trivial
due to two crucial issues: seamless information integration and domain-
specific generation. To this end, we propose a novel framework named URLLM,
which aims to improve the CDSR performance by exploring the User Retrieval
approach and domain grounding on LLM simultaneously. Specifically, we first
present a novel dual-graph sequential model to capture the diverse
information, along with an alignment and contrastive learning method to
facilitate domain knowledge transfer. Subsequently, a user retrieve-generation
model is adopted to seamlessly integrate the structural information into LLM,
fully harnessing its emergent inferencing ability. Furthermore, we propose a
domain-specific strategy and a refinement module to prevent out-of-domain
generation. Extensive experiments on Amazon demonstrated the information
integration and domain-specific generation ability of URLLM in comparison to
state-of-the-art baselines. Our code is available at
https://github.com/TingJShen/URLLM

Cross-Domain Sequential Recommendation, Large Language Model, Cold-Start
Recommendation

††copyright: acmcopyright††journalyear: 2017††doi:
XXXXXXX.XXXXXXX††conference: Proceedings of the 30th ACM International
Conference on Multimedia; October 10–14, 2022; Portugal, Lisbon††booktitle:
Proceedings of the 30th ACM International Conference on Multimedia (MM ’22),
October 10–14, 2022, Portugal, Lisbon††isbn: 978-X-XXXX-XXXX-X/18/06††ccs:
Information systems Information retrieval††ccs: Computing methodologies
Natural language generation

##  1\. Introduction

Sequential Recommendation (SR), focused on suggesting the next item for a user
based on their past sequential interactions to capture dynamic user
preferences, has gained significant attention in commercial, social, and
diverse scenarios (Zhang et al., 2022; Hidasi et al., 2016; Wang et al.,
2019). However, SR methods within a single domain usually encounter the long-
standing cold-start issue (Schein et al., 2002), i.e., it is challenging to
perform personalized recommendations for users with few interaction records.

To address the issue, Cross-Domain Sequential Recommendation (CDSR) has
garnered considerable attention in the field of recommendation systems, aiming
to mine and transfer users’ sequential preferences across different domains
(Gong et al., 2023; Yin et al., 2024a). Pioneer works like $\pi$-net (Ma et
al., 2019) and PSJNet (Sun et al., 2023a) focused on designing knowledge
transfer modules to capture cross-domain user preferences. Follow-up works
like MIFN (Ma et al., 2022) and DA-GCN (Guo et al., 2021) further borrowed the
powerful strength of Graph Neural Networks (GNNs) to model the high-order
relationship across domains. These methods have been demonstrated to be
effective for the CDSR problem. Despite the achieved results, most previous
works overlook the valuable semantic information buried in item features
(Alharbi and Caragea, 2022; Guo et al., 2021; Zhang et al., 2024), leading to
skewed user preferences. Recently, the powerful emergent capabilities (Boiko
et al., 2023) of Large Language Models (LLMs) have revolutionized the field of
recommendation systems (Chen, 2023), which can absorb item text features and
inject pre-trained common knowledge into recommendation systems. Meanwhile,
they can generate recommendations based on user preferences and historical
data, enabling interactive and explainable recommendations (Fan et al., 2023).
LLMs also offer the flexibility to design tuning strategies for specific
subtasks, such as determining whether to recommend an item or integrating
collaborative information. This integration aligns the sequential behaviors of
users with the language space, creating new modalities for recommendation
(Yuan et al., 2023; Lei et al., 2023). Therefore, we are motivated by these
encouraging capabilities to propose an LLM paradigm for the CDSR scenario.

However, harnessing the explicit capabilities of LLMs in the CDSR scenario is
non-trivial. Two essential issues arise: (1) Seamless Information Integration:
As shown in Figure 1, the CDSR task involves diverse formats of domain
information, including collaborative and semantic information. Items exhibit
intricate intrinsic structures respectively specified by the diverse
information. To fully leverage the emergent capabilities of LLMs, it is
crucial to integrate the structured information into LLMs in a seamless
manner. (2) Domain-Specific Generation. CDSR requires equipping the model with
domain-specific information and constraining the generation process to remain
confined within a specific domain. However, despite instructing the model to
generate items within a particular domain, the uncontrollable nature of LLMs
leads to 2% to 20% of generated content belonging to other domains. Figure 1
illustrates an optimal pipeline for domain-specific generation, wherein
distinct information aligns with specific generation processes.

Figure 1. An illustration of cold-start CDSA task along with the various forms
of information, the domain-specific demand on information and generation. The
line linking attributes represent the structural-semantic information.

Regarding issue (1), some efforts have been made to integrate collaborative
and semantic information with LLMs. Pioneer works like UniSRec (Hou et al.,
2022b) and CTRL (Li et al., 2023a) proposed to integrate semantic information
through a discriminative LLM like BERT. As for collaborative information,
BIGRec (Bao et al., 2023b) adopts a post-processing approach, integrating
through statistical results. RecInterpreter (Yang et al., 2023), LLaRA (Liao
et al., 2023), and CoLLM (Zhang et al., 2023a) tend to capture collaborative
information using an external traditional model and map it into the input
token embedding space of LLM. However, these methods fail to seamlessly
incorporate the emergent language reasoning capabilities of LLMs (Li et al.,
2023b; Wei et al., 2022), as evidenced by the misalignment between the pre-
trained knowledge representation of LLMs and their embedding representations.
This oversight is particularly evident in the absence of a seamless
integration of collaborative information and structural-semantic information.
Consequently, the integration of diverse information continues to pose a
substantial challenge. In addressing issue (2), existing retrieval
methodologies for LLMs, such as ICL (Li et al., 2023b) and Agent-based LLM
(Zhang et al., 2023b; Huang et al., 2024), primarily yield common sense
retrievals rather than domain-specific information. Furthermore, while the
existing LLM-based recommendation model (Bao et al., 2023c) (Bao et al.,
2023b) (Tang et al., 2023) acknowledge the cross-domain capabilities of LLMs,
they fail to impose constraints on the generation process of LLMs. This lack
of restriction results in out-of-domain generations, significantly undermining
the performance of CDSR.

Towards these challenges, in this paper, we propose a novel framework named
URLLM, addressing the cold-start issue by exploring a novel paradigm of User-
Retrieval approach and domain grounding on LLM simultaneously. Firstly, we
propose a dual graph sequence modeling model combining alignment and
contrastive learning on an item-attribute graph and three item-item domain
sequence graphs, aiming to model collaborative and structural-semantic
information. Subsequently, we present a KNN user retriever to retrieve
relevant user information for LLM. By leveraging LLM’s extensive reasoning and
few-shot learning capabilities in the integration of collaborative user
information, we can achieve a seamless integration of diverse information.
Finally, in order to preserve the domain-specific nature of both the input and
generated responses in the LLM, we propose a domain differentiation strategy
for user retrieval modules. Additionally, we introduce a refining mechanism to
enhance the outputs of the LLM.

Extensive experiments on two datasets, including movie-game and art-office on
Amazon following (Hou et al., 2022b) have demonstrated the effectiveness of
the proposed framework. Moreover, through analysis of experimental results, we
(1) recognize that the improvement brought by the types of information
integrated into components of URLLM positively correlated with the most
crucial information in the dataset. (2) find there existing positive relation
between the hit rate of retrieved users and the performance of the model. The
main contributions could be summarized as follows:

  * •

To our best knowledge, we are the first to study CDSR from a new perspective
on the user retrieval paradigm with seamless information integration and
domain-specific generation.

  * •

We develop a user retrieval bounded interaction paradigm between dual graph
sequence modeling models and LLM. With the aid of the module, we can integrate
structural-semantic and collaborative information into LLM in a seamless
manner.

  * •

We introduce a domain differentiation strategy for user retrieval modules and
a refinement module for the generated items of the LLM. The proposed module
ensures that the integrated user information and generation are tailored to
specific domains, aiming for domain-specific generation.

  * •

Extensive experiments on two public datasets and ablation analysis validate
that our URLLM framework unequivocally affirms the information integration and
domain-specific generation ability of our proposed framework.

##  2\. Related Work

###  2.1. Sequential Recommendation

Sequential recommendation is a technique that aims to delve into and
understand users’ interest patterns by analyzing their historical
interactions. Initially, techniques such as markov chain and matrix
factorization were employed (He et al., 2016). However, with the emergence of
neural networks, deep learning approaches like GRU4Rec (Hidasi and
Karatzoglou, 2018), Hypersorec (Wang et al., 2021) and Caser (Tang and Wang,
2018) were introduced to improve recommendation accuracy, while some efforts
are made with clustering (Han et al., 2023), denoising (Han et al., 2024) or
data regeneration (Yin et al., 2024b). Another notable technique in sequential
recommendation is the attention mechanism. SASRec (Kang and McAuley, 2018b)
and APGL4SR (Yin et al., 2023), for instance, utilize self-attention to
independently learn the impact of each interaction on the target behavior. On
the other hand, BERT4Rec (Sun et al., 2019a) incorporates bi-directional
transformer layers after conducting pre-training tasks. In recent years, graph
neural networks (GNNs) have gained attention for their ability to capture
higher-order relationships among items. GCE-GNN (Wang et al., 2020) constructs
local session graphs and leverages information from other sessions to create a
dense global graph for modeling the current session. SR-GNN
(DBLP:journals/corr/abs-1811-00855) employs gated GNNs in session graphs to
capture complex item transitions. To address the issue of data sparsity,
contrastive mechanisms have been adopted in some works. CL4SRec (Xie et al.,
2022) and CoSeRec (Liu et al., 2021) propose data augmentation approaches to
construct contrastive tasks, which help alleviate the sparsity problem.

###  2.2. Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to improve recommendation
performance for tasks involving items from different domains. Pioneering works
in this field include $\pi$-Net (Ma et al., 2019) and PSJNet (Sun et al.,
2023a), which employ sophisticated gating mechanisms to transfer single-domain
information. CD-SASRec (Alharbi and Caragea, 2022) extends SASRec to the
cross-domain setting by integrating the source-domain aggregated vector into
the target-domain item embedding. DA-GCN (Guo et al., 2021), a GNN-based
model, constructs a domain-aware graph to capture associations among items
from different domains. Hybrid models that combine various techniques to
capture item dependencies within sequences and complex associations between
domains have also been proposed. RecGURU (Li et al., 2022) introduces
adversarial learning to unify user representations from different domains into
a generalized representation. UniSRec (Hou et al., 2022b) leverages item texts
to learn more transferable representations for sequential recommendation. In
comparison, C2DSR (Cao et al., 2022a) employs a graphical and attentional
encoder to capture item relationships. It utilizes two sequential objectives,
in conjunction with a contrastive objective, to facilitate the joint learning
of single-domain and cross-domain user representations, achieving significant
progress.

###  2.3. LLM-based Recommendation System

Large Language Models (LLMs) have been widely adopted as recommender systems
to leverage item text features and enhance recommendation performance (Geng et
al., 2022; Liu et al., 2023; Wu et al., 2023). The majority of existing LLM-
based recommenders operate in a tuning-free manner, utilizing pretrained
knowledge to generate recommendations for the next item (Sun et al., 2023b;
Wang et al., 2023). For instance, CHAT-REC (Gao et al., 2023) employs ChatGPT
to grasp user preferences and enhance interactive and explainable
recommendations. GPT4Rec (Li et al., 2023c) utilizes GPT-2 to generate
hypothetical ”search queries” based on a user’s historical data, which are
then queried using the BM25 search engine to retrieve recommended items.
Another research direction in LLM-based recommendation focuses on designing
tuning strategies for specific subtasks. TALLRec (Bao et al., 2023c), for
example, employs instruction-tuning to determine whether an item should be
recommended. BIGRec (Bao et al., 2023b) adopts a post-processing approach
where recommendations are initially generated using LLM and then integrated
with collaborative information through an ensemble method. RecInterpreter
(Yang et al., 2023) and LLaRA (Liao et al., 2023) proposes a novel perspective
by considering the ”sequential behaviors of users” as a new modality for LLMs
in recommendation, aligning it with the language space. CoLLM (Zhang et al.,
2023a) captures collaborative information using an external traditional model
and maps it into the input token embedding space of LLM, creating
collaborative embeddings for LLM utilization.

Despite the significant progress achieved in the field of SR through
traditional or LLM-based methods, these approaches tend to focus on a limited
perspective of information formats. Consequently, there is an underutilization
of information pertaining to the cold-start feature of CDSR, which is the
primary focus of this paper. Moreover, although some previous works such as
Tallrec (Bao et al., 2023c), BIGRec (Bao et al., 2023b), and LLM-Rec (Tang et
al., 2023) recognize the cross-domain capabilities of LLMs, there is currently
a lack of an LLM-based structure specifically optimized for CDSR.

##  3\. PRELIMINARY

###  3.1. Problem Definition

In this work, we consider a general CDSR scenario, where users in the user set
$U=\\{u_{1},u_{2},...,u_{|U|}\\}$ have interactions with two product sets
$X=\\{x_{1},x_{2},...,x_{|X|}\\}$ and $Y=\\{y_{1},y_{2},...,y_{|Y|}\\}$. Each
user $u\in U$ has an interaction sequence
$S_{u}=[i_{1},i_{2},...,i_{t},...,i_{k}]$ representing the chronological order
of items in two domains. The primary objective of CDSR is to train the model
$M(X,Y,U,S)$ to predict the subsequent product $i_{k+1}\in{X\cup Y}$, where
$S=\\{S_{1},S_{2},...,S_{|U|}\\}$ denotes the cross-domain interaction
sequence.

###  3.2. Instruct Tuning on LLMs

In this paper, we use LLMs as the recommendation model $M$ for answer
formulation. The LLMs should be fine-tuned to adeptly adapt to the data
distribution and domain knowledge relevant to specific downstream tasks. The
fine-tuning process involves meticulously crafting instruction data to guide
the model’s output scope and format, as detailed below:

(1) $max_{\Phi}\sum_{x_{i},a_{i}\in
T}\sum_{t=1}^{|a_{i}|}log(P_{\Phi}(a_{i,t}|x,a_{i,<t})),$

where $\Phi$ is the parameters of LLM to be optimized, $T$ is the training
set, $a_{i,t}$ is the $t$-th token of the generated answer word, and $x$ is
the input context which contains an instruction and a query question.

##  4\. Methodology

Figure 2. The overall framework of URLLM. The component on the left showcases
exemplary prompts employed in graph construction and the similar user-
augmented LLM module. On the right, the process is delineated, wherein the
expansive reasoning and few-shot analogy capabilities of the LLM are
harnessed, concomitantly integrating structured knowledge.

To harness the inferential capabilities of Large Language Models (LLMs) for
blending LLMs with traditional models, we present URLLM illustrated in Figure
2. Initially, we introduce a Dual Graph Sequence-Modeling Model. This model
employs an LLM-enhanced item-attribute graph and an item-item sequence graph
to encapsulate collaborative and structural-semantic information. This type of
information is challenging to model with an isolated LLM. Then, we adopt a
User Retrieve-Generation Model to retrieve the most similar users from the
target domain and fuse structured text with collaborative information
seamlessly into LLM. Finally, to make the output of our model directly match
the real item in the correct domain, we combine BM25 (Robertson and Zaragoza,
2009) and the Dual Graph Sequence-Modeling Model to refine the generated
prediction.

###  4.1. Dual Graph Sequence Modeling Model

Aiming to model collaborative and semantic information, the construction of
this model contains a graph construction module, a graph alignment module, and
a contrastive self-attention module.

####  4.1.1. Graph Construction Module

In order to capture valuable collaborative and semantic information, including
their structural relationships, which can be effectively represented as a
graph, we construct a graph based on rules due to the absence of such data in
our dataset. The graph construction process comprises two modules: item-
attribute graph construction and item-item graph construction. These modules
establish collaborative relationships among items and structural-semantic
relationships, respectively. For item-attribute graph construction, the Chain-
of-Thought (COT) method (Wei et al., 2022) is employed to utilize an LLM for
item description. Subsequently, the model summarizes this description to
generate output attributes for the product. For instance, in Figure 2, for the
product I=Tinker Bell, the model provides an introduction denoted as
$LLM(I)=Intro$ and generates the attribute reusing $I$ as
$A_{I}=LLM(Intro,I)$. The undirected attribute graph, denoted as $G_{A}$, is
constructed by linking items with attributes formulated below:

(2) $G_{A}=(V,E),V=X\cup Y\cup A,E=\\{(v,t)|v\in X\cup Y,t\in A\\}.$

where $V$ and $E$ denotes the vector and edges of graph $G_{A}$.

For item-item graph construction, we construct $G_{S}=G_{X+Y}$, $G_{X}$ and
$G_{Y}$ respectively inspired by (Cao et al., 2022b). Firstly, given user
interaction sequence$S_{u}=[i_{1},...,i_{k}]$, we split the interaction
sequence into $S_{u}=S_{u},S^{X}_{u}=S_{u}\cap X,S^{Y}_{u}=S_{u}\cap Y$. We
then construct the directed item-item graph $G_{S},G_{X},G_{Y}$ by linking
items before and after using the formula below:

(3) $G_{S}=(V,E),V=X\cup Y,E=\\{(v,t)|\exists j,v=i_{j},t=i_{j+1}\\},$ (4)
$G_{X}=(V,E),V=X,E=\\{(v,t)|\exists j,v=i^{X}_{j},t=i^{X}_{j+1}\\},$ (5)
$G_{Y}=(V,E),V=Y,E=\\{(v,t)|\exists j,v=i^{Y}_{j},t=i^{Y}_{j+1}\\},$

where $V$ denotes the vector set, $E$ denotes the edge set, $i^{X}$ and
$i^{Y}$ denotes item in $S^{X}_{u}$ and $S^{Y}_{u}$.

####  4.1.2. Graph Alignment Module

Recognizing the deficiency of differential knowledge across distinct domains
in the existing item-attribute graph, it becomes imperative to investigate the
acquisition of domain transfer information to enhance the item-attribute
graphs. Domain transfer, in turn, enables the extraction of intricate
intrinsic structures that are specifically specified by diverse information
sources. However, the division of the item-attribute graph alone does not
provide distinct information. Therefore, in this section, we propose a Graph
Neural Network (GNN) that incorporates an alignment loss function to align and
integrate these fragmented pieces of information.

Given four graphs $G_{A},G_{S},G_{X},G_{Y}$, We first initialize the graph
embedding with $E_{A}^{0}\in\mathbb{R}^{|E_{A}^{0}|\times
d_{a}},E_{S}^{0}\in\mathbb{R}^{|E_{S}^{0}|\times
d},E^{0}_{X}\in\mathbb{R}^{|E^{0}_{X}|\times
d},E^{0}_{Y}\in\mathbb{R}^{|E^{0}_{Y}|\times d}$. Then, given these adjacency
matrix $A_{A},A_{S},A_{X},A_{Y}$, we construct $l$-layer GNN with output
denoted as $E^{l}$ as below:

(6) $\displaystyle E^{i}=Norm(A)E^{i-1},i\in[1,l],$

where $Norm(\cdot)$ represent the row-normalized function, $E^{i-1}$
represents the current graph convolutional layer and $E^{i}$ represents the
next layer. Then, to fully encapsulate the graphical information across
layers, we average the graph embeddings $E$ gaining $\hat{G}$ that yield item
representations as:

(7) $\displaystyle\hat{G}=\frac{1}{l}\Sigma_{i=1}^{l}(E^{i})+E^{0}.$

We apply this procedure to $G_{A},G_{S},G_{X},G_{Y}$ separately gaining
$\hat{G_{A}},\\\ \hat{G_{S}},\hat{G_{X}},\hat{G_{Y}}$, each with its
corresponding adjacency matrix $A$ and initialized graph embedding $E^{0}$.
Then, To resolve differences in input and output dimensions in alignment, we
design linear projection models $L,L_{X},L_{Y}$ with $d_{a}$ as input dim and
$d$ as output dim to reform the hidden representation. Finally, the alignment
loss function is designed below to transfer item-attribute graph knowledge of
domains $X$ and $Y$:

(8)
$\mathcal{L}_{al}=\Sigma_{S_{X}}||L_{X}(\hat{G_{A}})-\hat{G_{X}}||+\Sigma_{S_{Y}}||L_{Y}(\hat{G_{A}})-\hat{G_{Y}}||.$

We then concat them together as the final representation $R$ of the item,
gaining
$R_{S}=Concat(L(\hat{G_{A}}),\hat{G_{S}}),R_{X}=Concat(L_{X}(\hat{G_{A}}),\hat{G_{X}}),\\\
R_{Y}=Concat(L_{Y}(\hat{G_{A}}),\hat{G_{Y}})$. With the alignment above the
item-attribute and item-item graphs are modelized symmetrical and are
successfully aligned to specific domains.

####  4.1.3. Self-attention Contrastive Sequential Module

Figure 3. The example case illustrates the importance of LLM inferencing and
similar user retrieval.

Having successfully obtained dual graph item representations from the
aforementioned models, our objective is to capture users’ sequential
preferences based on their interaction sequences. This endeavor is crucial to
promote the retrieval of analogous user behaviors, thereby ensuring the
optimal utilization of the few-shot capabilities of LLMs. Similar to SASRec
(Kang and McAuley, 2018a), we employ a multi-head self-attention layer and
point-wise feed-forward layer to distinctly capture user preferences and
enhance retrieval precision. The formal definition of sequence modeling is
provided below:

(9) $\displaystyle
H_{S}=Attention_{S}(S_{u},R_{S}),H_{X}=Attention_{X}(S_{u}^{X},R_{X}),$
$\displaystyle H_{Y}=Attention_{Y}(S_{u}^{Y},R_{Y}),$

Denoting user preferences in the full domain, domain X, and domain Y as
$H_{S},H_{X},H_{Y}$ respectively, the training target of the sequential module
focuses on predicting the next model. We incorporate a discriminator
$D_{X},D_{Y}$ with a softmax function to assign scores to each item and then
maximize the score for the ground-truth answer. The training loss is defined
as follows:

(10)
$\mathcal{L}_{t}=\left\\{\begin{array}[]{lr}-log(P(i_{k}|D_{X}(H_{S}+H_{X})))&i_{k}\in
X\\\ -log(P(i_{k}|D_{Y}(H_{S}+H_{Y})))&i_{k}\in Y.\end{array}\right.$

where $i_{k}$ denotes the item to be recommended.

To handle the negative transfer challenge on Cross-Domain Sequential
Recommendation (CDSR), we adopt sequence corruption and random noise
integration to gain negative sequence samples. The formalized definition is
demonstrated below:

(11)
$\displaystyle\hat{H_{X}}=Attention_{X}(\hat{S_{X}},R_{X})+\Delta,\hat{H_{Y}}=Attention_{B}(\hat{S_{Y}},R_{Y})+\Delta,$

where $\hat{S_{X}}$ and $\hat{S_{Y}}$ randomly replaces X and Y domain items.
$\Delta$ is a random noice with $\left\|\Delta\right\|_{2}=\epsilon$ and
$\Delta=\bar{\Delta}\odot
sign(\mathbf{e}_{i}),\bar{\Delta}\in\mathbb{R}^{d}(0,1)$. The contrastive
training loss is denoted as below:

(12) $\begin{aligned}
\mathcal{L}_{ct}&=\Sigma(-log(D_{X}(H_{S}+H_{X}))+(1-D_{X}(H_{S}+\hat{H_{Y}})))\\\
&+\Sigma(-log(D_{Y}(H_{S}+H_{Y}))+(1-D_{Y}(H_{S}+\hat{H_{X}})))\end{aligned}.$

to maximize the infomax objective between domain preferences A and B. The
final optimization loss is as follows:

(13)
$\mathcal{L}=\mathcal{L}_{t}+\lambda\mathcal{L}_{ct}+\gamma\mathcal{L}_{al}.$

By combining contrastive alignment methodology for dual graph inherent
knowledge preferences $H_{S},H_{A},H_{B}$, which is hard for LLM to model.
Furthermore, the dual graph sequence-modeling model is also a recommendation
model with answer $I_{1}=[i_{1},...,i_{|I|}]$, and the answer can be utilized
to refine LLM’s generation afterward.

###  4.2. User Retrieve-Generation Model

In the preceding section, we obtained high-quality structured user
preferences. However, LLM cannot directly leverage this representation
information. In order to fully exploit the emergent capabilities of LLMs, such
as few-shot learning and language inferencing, we need a model that translates
the representation information into textual information, preferably in the
form of similar example data. Therefore, the following retrieve-augmented-
generation contains a KNN retriever, a LoRA-tuned LLM augmented by the target
user and retrieved user interactions, and an answer refinement structure.

####  4.2.1. KNN retriver

Algorithm 1 Overall pseudo code of URLLM

1:Product in two domain $X$ and $Y$, suppose $X_{id}<Y_{id}$. Training user
set $U$, user interaction sequence $S$, user $u$

2:User’s next possible item list $I$

3:Train $M(S_{u},X,Y)=E^{\prime}_{U},E^{\prime}_{u},I_{1}$ $\triangleright$
Prompt I is used here

4:Retrieve $U_{r}=KNN(u,U,E)$, $U_{t}=KNN(U,U,E)$

5:Train $LLM$ with $U_{t}$ $\triangleright$ Prompt II is used here

6:$I_{2}=LLM(U_{r},S_{u})$

7:$maxI=max\\{I_{2}[0:m]\\},minI=min\\{I_{2}[0:m]\\}$

8:$I=I_{2}$

9:if $maxI>X_{id}$ in recommend domain A then

10: $I=I_{1}$

11:end if

12:if $minI>Y_{id}$ in recommend domain B then

13: $I=I_{1}$

14:end if

We employ a KNN retrieval model to query the training users using $KNN(u)$ to
retrieve its k-nearest neighbors, denoted as N, based on a distance function
$d(\cdot,\cdot)$, specifically using the inner product, as they are already
normalized beforehand. The formal retrieval procedure is presented below:

(14) $\displaystyle E^{\prime}_{X}=H_{S}+H_{X},E^{\prime}Y=H_{S}+H_{Y}$
$\displaystyle U_{r}=KNN(u,U,E)$
$\displaystyle=\left\\{\begin{array}[]{lr}Top_{k}(min(d({E^{\prime}_{X}}_{u},{E^{\prime}_{X}}_{u_{t}}),u_{t}\in
U))&domain\ A\\\
Top_{k}(min(d({E^{\prime}_{Y}}_{u},{E^{\prime}_{Y}}_{u_{t}}),u_{t}\in
U))&domain\ B,\end{array}\right.$

where $U$ denotes the user in the training set, $U_{r}$ denotes the retrieved
users.

####  4.2.2. User Retrieval Augmented LLM

After gaining high-quality similar users, we then integrate this high-quality
knowledge using few-shot learning, proven to be one of the most effective ways
to enhance the emergent ability of LLM. Specifically, we form the prompt as
‘There are similar users who bought these items:’ with
$S_{U_{r}}=\\{S_{u_{1}},...,S_{u_{k}}\\},u_{1},...,u_{k}\in U_{r}$, and ‘The
user has bought these items:’ with $S_{u}$. The actual prompt is illustrated
in prompt II, Figure 2. However, there exists a distribution shift between
original instruction tuning on the CDSR task and similar user integration CDSR
task. We adopt the same KNN retrieval model on training users but choose from
top-2 instead of top-1 (top-1 is the same user) to construct training prompts.
Suppose the prompt is denoted as $P$, the optimization of user-integration
tuning loss is as follows:

(15) $\mathcal{L}_{r}=max_{\Phi_{L}}\sum_{p\in
P}\sum_{t=1}^{|p|}log(P_{\Phi+\Phi_{L}}(p_{t}|p_{<t})).$

$\Phi_{L}$ is the LoRA parameters and we only update LoRA parameters during
the training process. However, due to the uncontrollability of LLM outputs,
even when prompting and training explicitly for the generation of products in
domain $X$, there is still about 2% to 20% output of products from domain $Y$
during actual inference. Therefore, a subsequent answer refinement module is
required to further optimize the outputs of the LLM.

####  4.2.3. Answer Refinement Structure

We first adopt a BM25 retrieval model to ground the space of recommendation
language space to actual item space with $I_{2}$. Subsequently, to stably
identify whether LLM has influenced out-of-domain items, we consider the top-m
grounding items. If one of the answers runs out of domain, we will consider
adopting $I_{1}$ from the dual graph sequence-modeling model. In our
experiment, we set m=5 to gain an answer stably. The overall algorithm is
delineated in algorithm 1.

Finally, by combining a dual graph sequence-modeling model and a controllable
answer selection model, we leverage the expansive reasoning and few-shot
analogy capabilities of the LLM on integrating collaborative user information.
This approach not only addresses the cold-start problem but also tackles the
challenges of aligning cross-domain information in collaborative
recommendation systems. To provide a clearer illustration of how different
components of URLLM contribute to its performance, we present example cases in
Figure 3.

##  5\. Experimental Settings

###  5.1. Datasets

To demonstrate the performance of our proposed model, we conduct experiments
on two publicly available datasets from the Amazon platform (Jin et al.,
2023). This forms two distinct cross-domain scenarios: Movie-Game and Art-
Office. The Movie-Game dataset, preprocessed by (Bao et al., 2023a), is
sparser and contains many cold-start users. The Art-Office dataset exhibits a
more standardized distribution and is preprocessed by (Hou et al., 2022a). We
further refine the latter by filtering users with fewer than 3 item
interactions. Detailed descriptions and statistics for both datasets111The
processed dataset will be open once accepted. are provided in Table 1.

Table 1. The detailed description and statistics of datasets. Scenarios | #Items | #Train | #Valid | #Test | Avg.length
---|---|---|---|---|---
Movie | 71067 |  35941 |  1775 |  3601 | 4.095
Game | 112233 | 3.277
Art | 18639 |  16000 |  1154 |  2000 | 6.386
Office | 19757 | 8.263

###  5.2. Baselines

Table 2. The overall performance of all baselines on the Movie-Game dataset. Baseline Type | Method | HR@1 | HR@5 | HR@10 | HR@20 | MRR | NG@1 | NG@5 | NG@10 | NG@20
---|---|---|---|---|---|---|---|---|---|---
Traditional Baselines | LightGCN | 0.0033 | 0.0064 | 0.0099 | 0.0147 | 0.0054 | 0.0033 | 0.0063 | 0.0087 | 0.0105
SASRec | 0.0013 | 0.0038 | 0.0049 | 0.0075 | 0.0029 | 0.0013 | 0.0025 | 0.0029 | 0.0035
CoSeRec | 0.0036 | 0.0112 | 0.0174 | 0.0251 | 0.0078 | 0.0036 | 0.0081 | 0.0090 | 0.0116
$C^{2}DSR$ | 0.0047 | 0.0124 | 0.0181 | 0.0268 | 0.0089 | 0.0047 | 0.0085 | 0.0104 | 0.0125
LLM-based Baselines | UniSRec | 0.0090 | 0.0220 | 0.0259 | 0.0287 | 0.0148 | 0.0090 | 0.0160 | 0.0173 | 0.0181
BIGRec | 0.0067 | 0.0194 | 0.0305 | 0.0444 | 0.0168 | 0.0067 | 0.0148 | 0.0183 | 0.0233
GPT4Rec | 0.0088 | 0.0322 | 0.0410 | 0.0515 | 0.0198 | 0.0088 | 0.0208 | 0.0234 | 0.0266
CoLLM | 0.0101 | 0.0202 | 0.0354 | 0.0455 | 0.0171 | 0.0101 | 0.0146 | 0.0193 | 0.0219
Our Work | URLLM | 0.0105 | 0.0333 | 0.0416 | 0.0522 | 0.0211 | 0.0105 | 0.0221 | 0.0248 | 0.0298
Table 3. The overall performance of all baselines on the Art-Office dataset. Baseline Type | Method | HR@1 | HR@5 | HR@10 | HR@20 | MRR | NG@1 | NG@5 | NG@10 | NG@20
---|---|---|---|---|---|---|---|---|---|---
Traditional Baselines | LightGCN | 0.0105 | 0.0225 | 0.0255 | 0.0325 | 0.0198 | 0.0104 | 0.0133 | 0.0163 | 0.0184
SASRec | 0.0055 | 0.0120 | 0.0175 | 0.0235 | 0.0100 | 0.0055 | 0.0069 | 0.0084 | 0.0108
CoSeRec | 0.0099 | 0.0217 | 0.0284 | 0.0357 | 0.0154 | 0.0099 | 0.0159 | 0.0181 | 0.0199
$C^{2}DSR$ | 0.0155 | 0.0275 | 0.0320 | 0.0415 | 0.0229 | 0.0155 | 0.0219 | 0.0234 | 0.0257
LLM-based Baselines | UniSRec | 0.0145 | 0.0245 | 0.0375 | 0.0525 | 0.0237 | 0.0145 | 0.0201 | 0.0242 | 0.0256
BIGRec | 0.0220 | 0.0315 | 0.0370 | 0.0480 | 0.0277 | 0.0220 | 0.0268 | 0.0286 | 0.0313
GPT4Rec | 0.0165 | 0.0360 | 0.0435 | 0.0525 | 0.0262 | 0.0165 | 0.0263 | 0.0287 | 0.0311
CoLLM | 0.0170 | 0.0347 | 0.0438 | 0.0493 | 0.0286 | 0.0170 | 0.0263 | 0.0295 | 0.0309
Our Work | URLLM | 0.0270 | 0.0400 | 0.0485 | 0.0595 | 0.0355 | 0.0270 | 0.0353 | 0.0371 | 0.0399

We compare the performance of URLLM with state-of-the-art traditional and LLM-
based CDSR methods to showcase its effectiveness. The baselines are as
follows:

####  5.2.1. Traditional Baselines

  * •

LightGCN (He et al., 2020) simplifies the design of Graph Convolution Networks
for collaborative filtering by focusing on the essential component of
neighborhood aggregation.

  * •

SASRec (Kang and McAuley, 2018a) uses a causal attention mechanism to model
sequential patterns.

  * •

CoSeRec (Liu et al., 2021) introduces two new informative augmentation
operators that leverage item correlations with contrastive sequence.

  * •

$\bm{C^{2}DSR}$ (Cao et al., 2022b) captures user preferences by
simultaneously leveraging intra- and inter-sequence item relationships through
contrasive item-item graphs.

####  5.2.2. LLM-based Baselines

  * •

UniSRec (Hou et al., 2022a) incorporates a lightweight item encoding
architecture and employs contrastive pre-training tasks to learn transferable
representations across domains.

  * •

GPT4Rec-LLaMA2 (Li et al., 2023c) uses BM25 grounding method to refine answer
into actual item space. We replaced the GPT2 with LLaMA2 to ensure a fair
comparison.

  * •

BIGRec (Bao et al., 2023b) grounds recommendation space of tuned LLM into real
item space by incorporating statistical information.

  * •

CoLLM (Zhang et al., 2023a)222We transformed the item rating model into an
item recommendation model by providing 5000 candidates and ranking them based
on scores. captures collaborative information using an external traditional
model and maps it into LLM by adapter.

###  5.3. Evaluation Metrics

Following previous works (Kang and McAuley, 2018a; Sun et al., 2019b; Zhou et
al., 2020), we leverage the leave-one-out method to calculate the
recommendation performance. Besides, we adopt the whole item set as the
candidate item set during evaluation to avoid the sampling bias of the
candidate selection (Krichene and Rendle, 2020). Then, we evaluate the Top-K
recommendation performance by Mean Reciprocal Rank (MRR) (Voorhees, 1999),
Normalized Discounted Cumulative Gain (NDCG) (Järvelin and Kekäläinen, 2002)
and Hit Rate (HR) (Waters, 1976).

###  5.4. Implementation Details

The instruction-tuning and model inference are conducted on 4 Tesla A100 40G
GPUs, which takes approximately 24 hours for training stage and 1 hour for
inferencing. On our dual graph sequence-modeling model, we set $d_{s}=128$ and
$d_{a}=32$ according to their scale and sparsity. $\gamma$ and $\lambda$ are
all set to 0.3 for balance. Across all generative LLMs, we finetune
LLaMA2-7B-chat with LoRA(Hu et al., 2021) with LoRA-rank=8 and LoRA-alpha=16
using Adam(Kingma and Ba, 2017) in a default learning rate of 1e-4. In the
item attribute gaining part, in order to gain extra information, we use
gpt-3.5-turbo-061 to gain introduction and attribute of the item.

##  6\. Results and Analysis

###  6.1. Overall Performance

In this subsection, we compare the overall performance of all methods, which
is presented in Table 2 and Table 3. We can draw the following conclusions
from the results: 1). LLM-based CDSR methods generally surpass traditional
methods, particularly in the sparser Movie-Game domain. This highlights the
LLMs’ ability to perform extensive reasoning and few-shot learning. 2).
Discriminative LLM-based method UniSRec underperforms compared to other
generative LLMs, suggesting emergent reasoning capabilities in generative
models. 3). In the Art-Office domain, LLMs incorporating collaborative
information (BIGRec, CoLLM) outperform methods without such information
(GPT4Rec), underscoring its importance in LLMs. The opposite trend exists in
the sparse domain Movie-Game, indicating that inappropriate information
integration has a detrimental effect on performance. 4). URLLM consistently
outperforms all baselines, demonstrating its effectiveness. Its superiority
over LLM-based baselines confirms the value of seamless information
integration and domain-specific generation.

Figure 4. Performance comparison is conducted in warm (left) and cold (right) scenarios for Movie-Game and Art-Office. ”UC” denotes the substitution of the retrieval model of URLLM with $\bm{C^{2}DSR}$, and ”w/o R” designates URLLM without user retrieval. Table 4. Ablation study on Movie-Game dataset. URLLM-SASRec, URLLM-$\bm{C^{2}DSR}$ denotes the substitution of the retrieval model of URLLM with SASRec or $\bm{C^{2}DSR}$, simultaneously represent URLLM w/o item graph or URLLM w/o attribute graph. Method |  HR1 |  HR5 |  HR10 |  HR20 |  MRR |  NG1 |  NG5 |  NG10 |  NG20
---|---|---|---|---|---|---|---|---|---
w/o LLM |  0.0069 |  0.0091 |  0.0108 |  0.0127 |  0.0082 |  0.0069 |  0.0080 |  0.0085 |  0.0090
w/o retrieve |  0.0088 |  0.0322 |  0.0410 |  0.0515 |  0.0197 |  0.0088 |  0.0208 |  0.0234 |  0.0266
w/o graph alignment |  0.0102 |  0.0331 |  0.0414 |  0.0522 |  0.0210 |  0.0102 |  0.0208 |  0.0243 |  0.0268
w/o answer refine |  0.0105 |  0.0302 |  0.0410 |  0.0510 |  0.0203 |  0.0105 |  0.0206 |  0.0241 |  0.0267
w/o domain-specific retrieval |  0.0074 |  0.0197 |  0.0269 |  0.0366 |  0.0143 |  0.0074 |  0.0131 |  0.0155 |  0.0180
URLLM-SASRec |  0.0099 |  0.0308 |  0.0409 |  0.0505 |  0.0201 |  0.0099 |  0.0208 |  0.0244 |  0.0266
URLLM-$C^{2}DSR$ |  0.0089 |  0.0313 |  0.0402 |  0.0489 |  0.0190 |  0.0089 |  0.0201 |  0.0229 |  0.0251
URLLM |  0.0105 |  0.0333 |  0.0416 |  0.0522 |  0.0211 |  0.0105 |  0.0221 |  0.0248 |  0.0298
Table 5. Ablation study on Art-Office dataset. URLLM-SASRec, URLLM-$\bm{C^{2}DSR}$ denotes the substitution of the retrieval model of URLLM with SASRec or $\bm{C^{2}DSR}$, simultaneously represent URLLM w/o item graph or URLLM w/o attribute graph. Method |  HR1 |  HR5 |  HR10 |  HR20 |  MRR |  NG1 |  NG5 |  NG10 |  NG20
---|---|---|---|---|---|---|---|---|---
w/o LLM |  0.0265 |  0.0345 |  0.0430 |  0.0505 |  0.0322 |  0.0265 |  0.0308 |  0.0336 |  0.0355
w/o retriever |  0.0165 |  0.0360 |  0.0435 |  0.0525 |  0.0262 |  0.0165 |  0.0263 |  0.0287 |  0.0311
w/o graph alignment |  0.0235 |  0.0415 |  0.0475 |  0.0530 |  0.0322 |  0.0235 |  0.0324 |  0.0344 |  0.0357
w/o answer refine |  0.0230 |  0.0415 |  0.0480 |  0.0560 |  0.0323 |  0.0230 |  0.0329 |  0.0349 |  0.0370
w/o domain-specific retrieval |  0.0235 |  0.0315 |  0.0365 |  0.0455 |  0.0289 |  0.0235 |  0.0274 |  0.0292 |  0.0307
URLLM-SASRec |  0.0175 |  0.0350 |  0.0420 |  0.0490 |  0.0263 |  0.0175 |  0.0267 |  0.0290 |  0.0307
URLLM-$C^{2}DSR$ |  0.0215 |  0.0375 |  0.0455 |  0.0550 |  0.0302 |  0.0215 |  0.0309 |  0.0328 |  0.0349
URLLM |  0.0270 |  0.0430 |  0.0485 |  0.0595 |  0.0355 |  0.0270 |  0.0353 |  0.0371 |  0.0399

###  6.2. Analysis on warm/cold start scenario

URLLM seeks to retrieve similar users into LLM, aiming to integrate additional
knowledge tackling cold-start scenarios of CDSR. To evaluate the achievement
of this goal, we conduct a detailed examination of the methods’ performance in
both warm and cold-start scenarios. In particular, users are categorized into
cold scenarios when the length of their domain-relevant interaction sequences
is less than 3. Other users are categorized into warm scenarios. To showcase
the efficacy of our model in cold-start situations, comparison among four
models are compared in Figure 4. We can draw three pieces of information: 1).
In comparison to the model devoid of the retrieval module (w/o R), URLLM
exhibits an enhancement in cold-start scenarios across both datasets. This
underscores the efficacy of the user-retrieval module in augmenting the
model’s performance. Notably, in cold-start scenarios on the Art-Office
dataset, our model surpasses its warm-start performance (0.0364 versus 0.0355
in MRR). This suggests that URLLM has the potential to convert cold-start
scenarios into warm-start ones.

2). We observe that, within the context of the movie-game dataset’s warm-start
setting, the performance of user retrieval based on $C^{2}DSR$ is inferior to
not performing any retrieval. This is attributed to $C^{2}DSR$’s inability to
model structural-semantic information, which consequently impairs its user
retrieval capability. We hypothesize that this deficiency stems from the
absence of semantic information modeling, introducing noise into LLMs,
particularly under warm-start conditions.

3). URLLM’s efficacy in cold-start scenarios is demonstrably superior compared
to the traditional $C^{2}DSR$ model. On the Movie-Game dataset, URLLM achieves
a remarkable 3.6-fold improvement, while on the Art-Office dataset, the
performance gap widens to a factor of 13. These substantial gains highlight
URLLM’s effectiveness in augmenting the model’s capabilities for dealing with
sparse data and limited user interaction.

###  6.3. Ablation study

To demonstrate the effectiveness of each component, we conduct an ablation
study to compare URLLM with seven variants: (1) w/o LLM: removing LLM to use
only dual graph sequence modeling model to recommend; (2) w/o retrieve:
removing retriever to adopt LLaMA2 itself; (3) w/o graph alignment: removing
the alignment loss by setting $\gamma=0$; (4) w/o answer refine: generating
without domain-specific refine; (5) w/o domain-specific retrieval: retrieving
users without domain differentiation strategy; (6) URLLM-SASRec: replacing
dual graph sequence modeling model with SASRec; (7) URLLM-$C^{2}DSR$:
replacing dual graph sequence modeling model with $C^{2}DSR$. The results are
presented in Table 4 and Table 5.

The results demonstrate that each component within our framework plays a
positive role in enhancing overall performance. Notably, for the Art-Office
data, removing the retrieval and answer refinement module significantly
decreased recommendation effectiveness. This highlights the critical role of
collaborative information, employed in both retrieval and refining.
Conversely, in the sparser Movie-Game dataset, the LLM and attribute graph
exhibited the most significant contributions, suggesting that semantic
information is paramount for dealing with cold-start scenarios.

Table 6.  Movie-Game CDSR performance replacing on user retrieval with candidates. The subscript model in the Method denotes the application of the model for either user retrieval or the generation of top-k candidates. Method | UHR($10^{-3}$) | HR@1 | HR@5 | HR@10 | HR@20 | MRR | NG@1 | NG@5 | NG@10 | NG@20
---|---|---|---|---|---|---|---|---|---|---
$Candidate_{C^{2}DSR}$ | 0.4700 | 0.0083 | 0.0277 | 0.0369 | 0.0450 | 0.0180 | 0.0083 | 0.0183 | 0.0213 | 0.0232
$Retrieval_{C^{2}DSR}$ | 0.3694 | 0.0089 | 0.0313 | 0.0402 | 0.0489 | 0.0190 | 0.0089 | 0.0201 | 0.0229 | 0.0251
$Candidate_{URLLM}$ | 0.6350 | 0.0089 | 0.0275 | 0.0378 | 0.4580 | 0.0184 | 0.0089 | 0.0185 | 0.0219 | 0.0240
$Retrieval_{URLLM}$ | 0.5651 | 0.0105 | 0.0333 | 0.0416 | 0.0522 | 0.0211 | 0.0105 | 0.0221 | 0.0248 | 0.0298
Table 7.  Art-Office CDSR performance replacing on user retrieval with candidates. The subscript model in the Method denotes the application of the model for either user retrieval or the generation of top-k candidates. Method | UHR($10^{-3}$) | HR@1 | HR@5 | HR@10 | HR@20 | MRR | NG@1 | NG@5 | NG@10 | NG@20
---|---|---|---|---|---|---|---|---|---|---
$Candidate_{C^{2}DSR}$ | 1.5992 | 0.0185 | 0.0325 | 0.0370 | 0.0440 | 0.0257 | 0.0185 | 0.0260 | 0.0274 | 0.0292
$Retrieval_{C^{2}DSR}$ | 0.8783 | 0.0215 | 0.0375 | 0.0455 | 0.0550 | 0.0302 | 0.0215 | 0.0309 | 0.0328 | 0.0349
$Candidate_{URLLM}$ | 2.1511 | 0.0200 | 0.0340 | 0.0495 | 0.0455 | 0.0274 | 0.0200 | 0.0277 | 0.0295 | 0.0310
$Retrieval_{URLLM}$ | 1.6876 | 0.0270 | 0.0430 | 0.0485 | 0.0595 | 0.0355 | 0.0270 | 0.0353 | 0.0371 | 0.0399

Furthermore, when we dissociate the domain-specific retrieval method or
substitute the Dual Graph Sequence Modeling Model with SASRec and
$C^{2}DSR$—which fail to generate user representation in specific domains—the
model’s performance deteriorates due to the loss of crucial collaborative
information and the integration of noise. The answer refinement model also
affects the results of the model as shown in Table 4 and Table 5, which shows
the importance of domain-specific generation.

###  6.4. Analysis on integration of LLM

####  6.4.1. Analysis on quality of user integration

In general, superior-performing models tend to produce more precise user
representations during training, which in turn leads to improved user
retrieval results. Nevertheless, there appears to be an inconsistency between
the model performance depicted in Table 2 and the retrieval outcomes presented
in Table 4. While $C^{2}DSR$ outperforms SASRec in terms of retrieval
performance, its effectiveness diminishes when used as a replacement for the
search model than SASRec. This observation prompts us to explore the impact of
the retrieval result quality on recommendation performance.

To quantitatively assess performance, we employ User Hit Rate (UHR) dividing
the hit rate of the user interaction by the interaction length for retrieval
quality and Mean Reciprocal Rank (MRR) for recommendation effectiveness. We
further select a diverse subset of Art-Office examples, encompassing both
higher-quality and lower-quality retrievals. Figure 5 illustrates the observed
relationship between retrieval result quality and recommendation performance.
Notably, this experiment is not conducted on the Movie-Game dataset due to its
sparsity, which leads to unstable retrieval results and compromises the
reliability of sampled outcomes.

Figure 5. The positive correlation between quality of retrieved user and
performance of model.

Figure 5 reveals that URLLM outperforms $C^{2}DSR$, suggesting its superior
ability to model collaborative information. However, the close alignment of
their curves indicates that model choice becomes less impactful. Notably, the
positive correlation between MRR and UHR underscores the influence of
retrieval quality on performance.

Therefore, we can now address the initial question posed in this subsection:
even though a stronger model leads to
$HR_{SASRec}=0.0067<HR_{C^{2}DSR}=0.0097$, the length of retrieved user
$l_{SASRec}=14.41<l_{C^{2}DSR}=26.26$ resulting in $UHR_{1}/UHR_{2}=1.24>1$,
this causes a reduction of the $C^{2}DSR$ retrieval model.

####  6.4.2. Analysis on other linguistic integration of user integration

We replaced user retrieval with candidate provision to explore more semantic
integration approaches, ensuring that their lengths are equal. The model’s
performance using candidates can be observed in Table 6 and Table 7. It is
noteworthy that, even though the UHR value for candidates is higher than our
user retrieval under the same length, their overall final performance is
inferior to our model. Additionally, in the experiments, we discovered some
interesting phenomena – In a limited subset of instances even when the user
retrieval UHR is 0, our model’s performance still improves. This demonstrates
the importance of collaborative information in user retrieval and validates
the correctness of our approach.

##  7\. Conclusion

In conclusion, this paper introduced URLLM, a novel user retrieval CDSR
framework to seamlessly incorporate diverse information into LLM. Initially,
we developed a dual-graph sequence-modeling framework to capture collaborative
and structural-semantic information derived from user interactions.
Subsequently, we devised a novel user retrieval model for LLMs, aimed at
infusing this knowledge into LLMs by exploiting their robust language
reasoning and ensemble capabilities. Furthermore, the domain-specific
retrieval strategy and answer refinement module were proposed for domain-
specific information integration and generation.

Compared to traditional approaches and other LLM-based methods, URLLM
exhibited improved performance on the CDSA task. This research not only
explored the relationship between provided user retrieval results and model
performance but also propelled LLM-based CDSR toward the desired format. In
further work, we will attempt to retrench the retrieved user length and
evaluations on larger-scale models.

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