# Dual Intent Enhanced Graph Neural Network for Session-based New Item
Recommendation

Di Jin  College of Intelligence and Computing, Tianjin UniversityChina
<EMAIL_ADDRESS>,  Luzhi Wang  College of Intelligence and Computing, Tianjin
UniversityChina<EMAIL_ADDRESS>,  Yizhen Zheng  Department of Data
Science and AI, Faculty of IT, Monash UniversityAustralia
<EMAIL_ADDRESS>,  Guojie Song  School of Intelligence Science and
Technology, Peking UniversityChina<EMAIL_ADDRESS>,  Fei Jiang
MeituanChina<EMAIL_ADDRESS>,  Xiang Li  BeijingChina<EMAIL_ADDRESS>,
Wei Lin  BeijingChina<EMAIL_ADDRESS>and  Shirui Pan  Griffith
UniversityAustralia<EMAIL_ADDRESS>
(2023)

###### Abstract.

Recommender systems are essential to various fields, e.g., e-commerce,
e-learning, and streaming media. At present, graph neural networks (GNNs) for
session-based recommendations normally can only recommend items existing in
users’ historical sessions. As a result, these GNNs have difficulty
recommending items that users have never interacted with (new items), which
leads to a phenomenon of information cocoon. Therefore, it is necessary to
recommend new items to users. As there is no interaction between new items and
users, we cannot include new items when building session graphs for GNN
session-based recommender systems. Thus, it is challenging to recommend new
items for users when using GNN-based methods. We regard this challenge as “GNN
Session-based New Item Recommendation (GSNIR)”. To solve this problem, we
propose a dual-intent enhanced graph neural network for it. Due to the fact
that new items are not tied to historical sessions, the users’ intent is
difficult to predict. We design a dual-intent network to learn user intent
from an attention mechanism and the distribution of historical data
respectively, which can simulate users’ decision-making process in interacting
with a new item. To solve the challenge that new items cannot be learned by
GNNs, inspired by zero-shot learning (ZSL), we infer the new item
representation in GNN space by using their attributes. By outputting new item
probabilities, which contain recommendation scores of the corresponding items,
the new items with higher scores are recommended to users. Experiments on two
representative real-world datasets show the superiority of our proposed
method. The case study from the real-world verifies interpretability benefits
brought by the dual-intent module and the new item reasoning module.

GNN; Session-based Recommendation; New Item Recommendation

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
ACM Web Conference 2023; May 1–5, 2023; Austin, TX, USA††booktitle:
Proceedings of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin,
TX, USA††price: 15.00††doi: 10.1145/3543507.3583526††isbn:
978-1-4503-9416-1/23/04††ccs: Information system Data mining

##  1\. Introduction

Recommender systems have a major impact on a variety of fields, including
e-commerce (ge2020understanding, ; kersbergen2021learnings, ), e-business
(dias2008value, ), and media streaming services (wang2021clicks, ), which can
influence our purchasing decisions and even lifestyles. In recommender
systems, the session-based recommendation is a common task (wu2019session, ;
wang2020global, ). Based on the previous items a user has interacted with in a
session (i.e., a sequence of items), session-based recommendations aim at
predicting the next item a user will interact with. Early approaches are based
on identifying frequent sequential patterns, which are used for next-item
recommendations in e-commerce or music domains (wang2021denoising, ;
DBLP:journals/csur/BonninJ14, ). Such methods are computationally expensive
because they are parameter-sensitive algorithms
(DBLP:journals/umuai/LudewigJ18, ). In recent years, some works have used
Recurrent Neural Networks (RNNs) for a session-based recommendation. For
example, Hidasi et al. (DBLP:journals/corr/HidasiKBT15, ) use Gated Recurrent
Units (GRUs) as a special form of RNNs to model sparse session data to predict
the next user interactions in a session. However, recommender systems based on
RNNs have some limitations. For example, the number of interactions a user
makes in a session is limited. It is difficult for RNNs to accurately estimate
each user’s preference from each session (wu2019session, ). Recently, graph
neural networks (GNNs) are developing very fast and have been applied in many
real-world applications (tan2023federated, ; liuyue_DCRN, ;
zheng2022rethinking, ; zheng2022unifying, ). In session-based recommendations,
GNN-based approaches become increasingly popular as they can address the
aforementioned issues and improve prediction results.

Currently, there are many GNN-based methods for session-based recommendation,
such as SR-GNN (wu2019session, ), GCE-GNN (wang2020global, ) and GC-SAN
(xu2019graph, ). These GNN-based models typically construct a user’s
historical sessions as a session graph. Then, a well-designed graph neural
network learns the representations of users’ intent and items from the
historical session graph, and computes user-item matching scores. Finally, the
recommender system calculates the probability that the user’s preference for
items based on the match score. However, these methods only recommend items
that already exist in the session (old items), which can easily reduce the
desire of users to interact with items and lead to information cocoons.
Specifically, these methods take in the items in the user’s historical session
as input and output the probability of historical items to be recommended as
the next recommended items. For example, in Fig. 1 (a), the input of SR-GNN is
$v_{1},v_{2},v_{3},v_{4}$, and its output is the probability of
$v_{1},v_{2},v_{3},v_{4}$. Therefore, users can be tired of the old items
easily.

Figure 1. Difference between session-based next item recommendation and GSNIR.
The dashed line points to the ground truth items. In (a), the session is
$v_{1}\rightarrow v_{2}\rightarrow v_{3}\rightarrow v_{1}\rightarrow v_{4}$,
and $v_{3}$ is the ground truth item. SR-GNN calculates the probability of
each item that is liked by the user. In (b), $v_{1},v_{2},v_{3}$ are old
items, $v_{4},v_{5},v_{6}$ are candidate new items, where $v_{4}$ is the
ground truth new item. NirGNN calculates the selected probability by user for
every candidate new items.

To expand users’ selection for session-based recommendations, we introduce a
new challenge “GNN Session-based New Item Recommendation (GSNIR)”. In
specific, GSNIR refers to predicting whether a user will interact with a new
item in the future (e.g., purchase and click) based on his or her previous
interaction. In Fig. 1, we present the comparison between the traditional GNN-
based methods (e.g. SR-GNN) for session-based recommendation and our proposed
new setting. Even though new item recommendation is very important to improve
user experience, existing GNN-based models perform poorly in this setting. As
we mentioned before, these GNN methods recommend old items, and they learn old
item embeddings in session graphs. But for a new item that the user has not
interacted with, it is not in the session, so it cannot use topology
information to learn the embedding. Therefore, it is necessary to design a new
GNN-based recommender system to solve the GSNIR.

The GSNIR meets many challenges: Challenge 1.  How to simulate the decision-
making process of users when they choose new items? When a user interacts with
a new item, the user may tend to substitute the old item with its relevant
attributes or look for substitutes in a large taxonomy. For example, if a user
often buys coca-cola, when he/she buys a new product, he/she may choose pepsi
as a substitution. Moreover, he/she may look for alternatives under the
taxonomy subtree for beverages and choose coconut water. By learning user
intent, we can filter new items that match the user’s preference. However,
human behavior is ever-changing, which makes it difficult to learn user intent
accurately from the user’s historical session. Challenge 2. How can we obtain
effective new item representations by GNNs? The new item recommendation
problem poses a challenge to learning representation based on limited
information. As there is no interaction between new items and users, it is
difficult to include new items when building a session graph for GNN encoding.

To address the aforementioned problem, in this paper, we propose a novel GNN-
based model, namely New Item Recommendation Graph Neural Network (NirGNN).
This model can be divided into two components. The first component is to learn
the user intent effectively. To do this, NirGNN proposes a dual-intent
learning method. NirGNN introduces the taxonomy tree of each item to determine
the general direction of the user intent. By using both the attention
mechanism and the $\beta$ distribution on taxonomy data, the method predicts
whether the user prefers the taxonomy for the interaction guide or the item
itself. The second component is to obtain new item representation. Inspired by
zero-shot learning (ZSL), we design a reasoning method to map the independent
new items into the GNN space by using item attributes, which enables new-item
recommendation. To evaluate the effectiveness of NirGNN, we provide two new
real-world datasets for the new item recommendation setting and conduct
experiments on both datasets. Experimental results demonstrate the
effectiveness of our method. Besides, we provide a case study in a commercial
recommendation environment, which demonstrates the interpretability and
practicability of our model. In summary, the contributions of this paper can
be summarised as follows:

  * •

To the best of our knowledge, we are the first to propose the GNN session-
based new item recommendation for the session-based recommendation scenario.

  * •

We propose a dual-intent enhanced graph neural network to tackle GSNIR. The
dual-intent strategy learns user intent from users’ attention and data
distribution respectively, which provides a different perspective on user
preference.

  * •

We introduce zero-shot learning into a session-based recommender system for
the first time, which solves the problem that new items cannot be encoded by
GNN-based methods due to the lack of interaction with users.

  * •

Extensive experiments conducted on session-based recommendation datasets show
the superiority of NirGNN. Moreover, we present a real-world case study to
demonstrate our method’s interpretability and effectiveness.

##  2\. Related Study

GNNs for session-based recommendation. Many types of session-based
recommendation methods have been proposed, e.g., Markov chains (zhanghe, ;
DBLP:journals/tkde/JinYJPHWYZ23, ; yu2019multi, ), and Recurrent Neural
Networks (RNNs) (liu2021case4sr, ; zhou2020cnn, ). In recent years, to improve
session-based recommendation, GNNs (wang2023contrastive, ;
DBLP:conf/ijcai/JinWZLJLP22, ; DBLP:conf/www/JinHL021, ;
DBLP:conf/icdm/YuJLHWT021, ) have been incorporated into session-based
recommender systems to model complex relations between adjacent items. For
example, as the first work introduces GNNs into the session-based
recommendation, SR-GNN (wu2019session, ) transforms each session into a
session graph and uses a GNN to capture the complex relations of items in the
graph. GC-SAN (xu2019graph, ) extends SR-GNN by incorporating multi-layer
self-attention with residual connection to model the long-term preference.
GCE-GNN (wang2020global, ) combines the global session graph with the local
session graph and position embedding to better represent the feature of items.
COTREC (xia2021self, ) utilizes contrastive learning and develops a self-
supervised learning with co-training method for session-based recommendations.

Zero-shot learning. ZSL simulates the way of human reasoning to recognize new
things that have never been seen before in CV (chen2021free, ;
han2021contrastive, ; naeem2021learning, ), NLP (cheng2021data, ; liu2021task,
; geng2021ontozsl, ), and GNN (mancini2022learning, ; gao2020ci, ;
liu2020attribute, ). For example, through the shape information of horses, and
stripe information of tigers, the ZSL model is capable of identifying zebra
images. Lampert et al. (lampert2009learning, ) encodes attribute
representations of images and then uses these attribute embeddings to do
classification inference based on human-specified attribute descriptions of
the test images. ABS-Net (lu2018attribute, ) generates pseudo feature
representations for unseen images. With the pseudo feature as a data
augmentation approach, ABS-Net can tackle both zero-shot learning and
conventional supervised learning.

##  3\. Problem Statement

###  3.1. Preliminary

A user historical session $S=\\{v_{1},v_{2},v_{3},...,v_{n}\\}$ consists of
items that the user has interacted with (old items), each item $v_{i}$ in $S$
is sorted by timestamp, which rigidly indicates the order of items occurring
in the $S$. The historical session can be modeled as a directed session graph
$G=(V,E)$ where $|V|=n$, and an edge $e(v_{i},v_{j})\in E$ represents that the
user interacts vertex $v_{j}$ after interacting vertex $v_{i}$. Given a set of
new items $C=\\{c_{1},c_{2},...,c_{m}\\}$ that have never been interacted
with, the GSNIR aims to recommend new items to users. Specifically, for any
session graph $G$, the output of the session-based new item recommendation
model is a ranked new item list with the predicted likelihood of each new
item. The $top-k$ new items with the highest probability in $C$ will be
recommended to the user. Each item $v_{i}$ contains a taxonomy tree, including
three different levels, namely $t_{i1},t_{i2},t_{i3}$, representing different
granularity levels from large to small. For example, in a food recommender
system, the three taxonomy levels of a brand of coconut water are ‘Beverages’,
‘Bottled Beverages’, and ‘Coconut water’. The taxonomy information provides
external information, which makes GNNs limit-free to ontology information of
items in learning. In addition, each item contains a set of attribute
information $A=\\{a_{i1},...,a_{ik}\\}$. For example, the attribute of a
coconut water $v_{i}$ may include its brand and price attributes. Attribute
information is beneficial for us to infer new item embeddings. The embedding
of the three-level taxonomy tree of item $v_{i}$ can be regarded as
$t_{i1},t_{i2},t_{i3}$, and the global taxonomy embedding of $v_{i}$ is
$t_{i}=W(t_{i1},t_{i2},t_{i3})$, where $W\in\mathbb{R}^{3d\times d}$ is a
Multi-layer Perceptron (MLP) to compress $t_{i1},t_{i2},t_{i3}$ into the
latent space $\mathbb{R}^{d}$. For attributes of items in the session and the
attributes of the new items, we use the word2vec model to process them. The
attribute embedding of old items and new items are $atr$ and $atr^{*}$
respectively.

###  3.2. Construct Session Graph

We follow the method proposed by SR-GNN to construct the session graph. The
items in the session sequence are arranged in order. Each item is denoted as a
vertex of the session graph, and each item and its next interacted item form
an edge. Edges are divided into in-degree and out-degree. For example, in
$v_{1}\rightarrow v_{2}$, $v_{1}$ is a start node and $v_{2}$ is an end node
of the edge. This edge is the outgoing edge of $v_{1}$ and the incoming edge
of $v_{2}$. Therefore, two adjacency matrices are needed to represent the
session graph. Each edge is assigned a normalized weight. The weight is
calculated as the occurrence of the edge divided by the indegree/outdegree of
that edge’s start node. For example, given a session $v_{1}\rightarrow
v_{2}\rightarrow v_{3}\rightarrow v_{1}\rightarrow v_{4}$, Fig. 2 (a) (b)
shows the details of session graph constructing. In the outgoing adjacency
matrix, the out-degree of $v_{1}$ is $2$, and $v_{1}\rightarrow v_{2}$ occurs
once, then the weight of $v_{1}\rightarrow v_{2}$ is $\frac{1}{2}$. An example
of an adjacency matrix is shown in Fig. 2. Fig. 2 (c) (d) is the outgoing
adjacency matrix and incoming adjacency matrix of (b), respectively. The
outgoing adjacency matrix and incoming adjacency matrix together construct
adjacency matrices for the session graph.

Figure 2. A constructed session graph and the adjacency matrices.  Figure 3.
Overview of NirGNN. In the first part of inputs (a), the taxonomy tree of each
item and the session graphs are fed into a GNN encoder (b) to obtain the
representation, and then (c) the dual-intents of the user are learned from the
perspective of attention and data distribution. In the second part of inputs
(a), attributes of items in the session graph and new item attributes are
input into a word embedding encoder (b). Then, the semantic information of
every item can be learned. Through a new item reasoning network (c), the
learned semantic information is used to infer new item embedding. Finally, the
new item embedding and user intent are used to calculate the score of each new
item (d), and the new item with the highest score is regarded as the final
recommendation. We introduce the four modules separately in the following
sections.

###  3.3. Problem Setting

The general session-based GNN recommender system is usually the next item
recommendation. That is, the input is a session containing items, and the
output is the predicted score for each item in the session. However, the input
of GSNIR is the historical session, and the output is the predicted score of
the candidate new items, which have not appeared in the historical records.

Since we cannot know what new items the user will interact with, we take the
last item of the historical session as the new item (ground truth) of
recommendation and mask it from the session. To ensure that the new item
appears for the first time to the user, we delete it from the historical
session and reconnect the historical session in the order in which the item
was interacted with by the user.

##  4\. Method

This section details our proposed New Item Recommendation Graph Neural Network
(NirGNN) model. Fig. 3 shows the overview of NirGNN.

###  4.1. Item Embedding

Since both the session graph and the taxonomy tree are directed graphs, we use
the session graphs constructing approach of SR-GNN (wu2019session, ) to
process the adjacency matrix of directed graphs. Then, we use a gated graph
session neural network (GGNN) as the core encoder to learn the representation
of each item and its taxonomy tree. The learning process of GGNN can be
summarised as follows (wu2019session, ):

(1)
$\begin{split}a^{(t)}_{i}&=A_{i:}\left[v^{(t-1)}_{1},\dots,v^{(t-1)}_{n}\right]^{\top}H+b,\\\
z^{(t)}_{i}&=\sigma\left(W_{z}a^{(t)}_{i}+U_{z}v^{(t-1)}_{i}\right),\\\
r^{(t)}_{i}&=\sigma\left(W_{r}a^{(t)}_{i}+U_{r}v^{(t-1)}_{i}\right),\\\
\widetilde{v^{(t)}_{i}}&=\tanh\left(W_{o}a^{(t)}_{i}+U_{o}\left(r^{(t)}_{i}\odot
v^{(t-1)}_{i}\right)\right),\\\ v^{(t)}_{i}&=\left(1-z^{(t)}_{i}\right)\odot
v^{(t-1)}_{i}+z^{(t)}_{i}\odot\widetilde{v^{(t)}_{i}},\end{split}$

where $\left[v^{(t-1)}_{1},\dots,v^{(t-1)}_{n}\right]$ is the list of item
vectors in session $s$, $t$ is the training step,
$A_{i:}\in\mathbb{R}^{1\times 2n}$ is the $i$-th row in matrix,
$H\in\mathbb{R}^{d\times 2d}$ and $b\in\mathbb{R}^{d}$ are weight and bias
parameter respectively, $z_{i}\in\mathbb{R}^{d\times d}$ and
$r_{i}\in\mathbb{R}^{d\times d}$ are the reset and update gates respectively,
$\sigma(\cdot)$ is the sigmoid function, and $\odot$ denotes element-wise
multiplication. For each session graph $G$, the GGNN model propagates
information between neighboring items.

###  4.2. Dual-Intent Nets

We learn the intent of the user from the perspective of an attention mechanism
and data distribution in the session graph respectively. The following is a
detailed introduction.

####  4.2.1. User $\alpha$ Intent Net

We first learn user intent from a soft-attention mechanism perspective. Since
most items interacted by the user recently are more likely to reflect the
short-term preferences of the user than the items interacted at an earlier
time, we extract the information of the last-interacted item $v_{n}$ and add
it to each item. In addition, the taxonomy tree information also provides a
macro guide for user intent. For a session graph, the intent of the user
$I_{\alpha}$ can be learned as:

(2) $I_{\alpha}=\sum_{i=1}^{n}\sigma((v_{i}\oplus t_{i})*W_{1}+(v_{n}\oplus
t_{n})*W_{2})(v_{i}\oplus t_{i}),$

where $\sigma(\cdot)$ is a sigmoid function, $\oplus$ is a concatenation
function, $t_{n}$ is the global taxonomy embedding of $v_{n}$, $W_{1}$ and
$W_{2}$ are the two intent filtering matrix. By applying the soft-attention
mechanism to the session graph, we obtain the $\alpha$ intent of the user.

####  4.2.2. User $\beta$ Intent Net

In a recommender system, the historical session of the user, and the taxonomy
tree/attributes of every item may be incomplete. To effectively mine the
user’s intent, only using the user $\alpha$ intent net is inadequate. The
Bayesian inference method is often used to make the information complete by
reasoning on new information. However, Bayesian inference is a very
complicated process that requires complex calculations. It is possible to
reduce the computational difficulty of the Bayesian inference if we use the
$\beta$ distribution. The $\beta$ distribution is conjugated before the
binomial likelihood. We can use the $\beta$ distribution in place of the prior
distribution, since the posterior distribution also follows the $\beta$
distribution, which reduces the cost of computation. Moreover, the $\beta$
distribution is a superposition of multiple binomial distributions, which is
also reflected in the conjugation of the binomial distribution. For example,
the daily behavior of users (interacting or not interacting) is a binomial
distribution, and many days are superimposed together to form a $\beta$
distribution. Thus, NirGNN calculates the posterior distribution by item
embedding $v_{i}$ and taxonomy embedding $t_{i}$ without multiplying the
likelihood by the prior assignment for user intent. We consider the user’s
preference from the perspectives of $v_{i}$ and $t_{i}$, and learn whether the
user prefers the item itself or its taxonomy tree. $v_{i}$ and $t_{i}$
constitute a set of joint distribution probabilities. The $\beta$ distribution
represents the probability that the user likes the joint distribution
probability of $v_{i}$ and $t_{i}$, that is, the probability that the user
likes $v_{i}$ and $t_{i}$ in a certain distribution state. For each vertex
$v_{i}$, user intent distribution $b_{i}$ is:

(3)
$\begin{split}b_{i}&=\mathcal{S}(\delta(v_{i},t_{i})x^{\phi(\varrho(v_{i}))-1}(1-x)^{\phi(\varrho(t_{i}))-1}),\\\
\vspace{0.5em}\delta&=\frac{\Gamma(\phi(\varrho(v_{i}))+\phi(\varrho(t_{i})))}{\Gamma(\phi(\varrho(v_{i})))\Gamma(\phi(\varrho(t_{i})))},\end{split}$

where $\mathcal{S}(\cdot)$ is the sample function, $x$ is a variable,
$\mathrm{\Gamma(\cdot)}$ is the Gamma distribution, $\varrho(\cdot)$ is a
Softplus activation function, $\phi(x)=log(1+e^{x})$ is a normalization
function used to adjust the distribution interval of the data. Specifically,
we use the activation function Softplus to process the embeddings of $v_{i}$
and $t_{i}$ and normalize them to the range of $(0,1)$. We mainly consider
whether the user is more interested in the representation of $v_{i}$ or the
representation of $t_{i}$. The joint probability of the representation of
$v_{i}$ and the representation of $t_{i}$ constitutes a set of probability
density functions. The $\beta$ distribution shows the probability distribution
that users are interested in $v_{i}$ or $t_{i}$ under different parameters. We
find the closest probability to the user’s true intent with sampling on
$\beta$ distribution. Therefore, from the perspective of data distribution,
$\beta$ distribution reflects the joint probability distribution of item and
taxonomy tree. From the perspective of multiple cumulative distributions, it
also reflects the intent of the user.

In a session graph, the recently interacted items are often more reflective of
the user’s recent interest than the items that interacted a long time ago.
Therefore, we add a soft-attention mechanism with $\beta$ distribution to
increase the priority of recently interacted item $v_{i}$.

(4) $v_{i}^{\prime}=b_{i}*(v_{i}\oplus t_{i})+b_{n}*(v_{n}\oplus t_{n}),$

where $v_{i}^{\prime}$ is the embedding of $v_{i}$ that is prioritized.
However, the above Eq.4 will weaken the probability of $v_{i}$, so we readjust
the probability distribution of each vertex so that they re-obey the $\beta$
distribution, which is equivalent to obtaining the attention that obeys the
$\beta$ distribution. The attention modified $\beta$ distribution of each item
$v_{i}$ is:

(5)
$\beta_{i}=\frac{v_{i}^{\prime}-\frac{1}{n}\sum_{i=1}^{n}(b_{i}*(v_{i}\oplus
t_{i}))}{\sqrt{\frac{\sum_{i=1}^{n}(b_{i}-\mathrm{avg}(b))^{2}}{n}}}W_{3},$

where $\mathrm{avg(\cdot)}$ is the average function, and $W_{3}$ is a MLP to
dimensionality reduction. After we get the modified $\beta$ distribution, we
can get the user intent expressed by items in the session graph. The $\beta$
intent of the user can be defined as:

(6) $I_{\beta}=\sum_{i=1}^{n}(v_{i}\oplus t_{i})*\beta_{i}.$

By combining the intent from the two perspectives of $\alpha$ intent and
$\beta$ distribution intent, we design a weighted intent $I$:

(7) $I=\lambda I_{\alpha}+(1-\lambda)I_{\beta},$

where $\lambda\in[0,1]$ is a parameter that controls the weight of each
intent.

###  4.3. New Item Reasoning Network

Figure 4. The processing of new item reasoning. The left is the attribute
space, and the right is the embedding space.

Since the new items are time-independent, we cannot directly use GNN to embed
the new item, which makes it impossible to calculate the similarity of user
intent to the new item. To this end, we try to use the semantic information of
the new item attribute to infer the embedding of the new item. Inspired by
zero-shot learning, we can establish the spatial transformation relationship
between item semantic attributes and item embedding. Specifically, given the
attribute semantics $atr_{i}$ of old items $v_{i}$, we learn a transformation
function $\theta$ to map the semantic attribute information to the item
embedding space. Specifically, the generated item embedding can be calculated
as $v^{*}_{i}=\theta(atr_{i})$. We implement $\theta$ as a MLP layer. In order
to ensure that the generated item embedding is in the same space as the
original embedding, we try to reduce the gap between original embedding
$v_{i}$ and generated embedding $v^{*}_{i}$. We measure the gap between them
with an improved Bhattacharyya distance, which gives an upper bound on the
minimum error rate for the separability of the two discrete probability
distributions. The improved Bhattacharyya distance $BC_{imp}$ is:

(8)
$\displaystyle\begin{split}BC_{imp}(v_{i},v_{i}^{*})=\left\\{\begin{array}[]{ll}-log(\sqrt{v_{i}*v_{i}^{*}})&,\sqrt{v_{i}*v_{i}^{*}}\neq\varnothing.\\\
0&,otherwise.\end{array}\right.\end{split}$

Fig. 4 shows the process of reasoning. To improve the transformation function
$\theta$ from semantics attribute space to item embedding space, we design a
loss function $L_{zero}$ to optimize the difference between the original item
embedding and the generated item embedding from the item semantics attributes.

(9) $L_{zero}(v_{i},v_{i}^{*})=BC_{imp}(v_{i},v_{i}^{*}).$

Then we use this transformation function to reason the new item embedding by
the attribute information of the new item. The embedding of the new item
$c_{i}$ can be inferred by the spatial transform function
$c_{i}=\theta(atr_{i}^{*})$, where $atr_{i}^{*}$ is the attribute embedding of
new item $c_{i}$.

###  4.4. Prediction

Now, we obtain the new item embedding $c_{i}$ and user intent from the above
process. The recommendation score for each new item can be calculated by
multiplying new item embeddings $c_{i}$ by user intent $I$, which can be
defined as:

(10) $\hat{Z_{i}}=\mathrm{softmax}(I^{\top}*c_{i}).$

The final loss consists of the CrossEntropy Loss $L_{ce}$, which is used to
calculate the difference between the new item score and the ground truth, and
$L_{zero}$, which is used to optimize the inference ability of the spatial
transfer function:

(11) $L=\gamma
L_{ce}(Z,\hat{Z})+(1-\gamma)(\sum_{i=1}^{n}L_{zero}(v_{i},\theta(atr_{i}))),$

where $Z$ is the one-hot encoding vector of the ground truth item, $\hat{Z}$
denotes the recommendation scores for overall new items, and $\gamma\in[0,1]$
is the weight to control the importance of losses.

Table 1. Dataset Description

Datasets | Items | Train sessions | Test sessions | Average length
---|---|---|---|---
Amazon G&GF | 18889 | 51958 | 37813 | 8.394
Yelpsmall | 14726 | 19035 | 2311 | 4.666

##  5\. Experiment

Table 2. Experiments on Amazon G&GF and Yelpsmall dataset.

Methods | Amazon G&GF | Yelpsmall
---|---|---
P@20 | MRR@20 | P@10 | MRR@10 | P@20 | MRR@20 | P@10 | MRR@10
SR-GNN | 1.438$\pm$0.134 | 0.336$\pm$0.025 | 0.086$\pm$0.078 | 0.297$\pm$0.021 | 0.764$\pm$0.534 | 0.192$\pm$0.118 | 0.476$\pm$0.346 | 0.180$\pm$0.111
SR-GNN-ATT | 0.678$\pm$0.202 | 0.157$\pm$0.038 | 0.375$\pm$0.144 | 0.136$\pm$0.040 | 1.631$\pm$0.353 | 0.410$\pm$0.089 | 0.989$\pm$0.183 | 0.366$\pm$0.078
GC-SAN | 2.028$\pm$0.108 | 0.580$\pm$0.093 | 1.288$\pm$0.059 | 0.529$\pm$0.090 | 0.793$\pm$0.115 | 0.174$\pm$0.094 | 0.418$\pm$0.144 | 0.165$\pm$0.103
GCE-GNN | 1.650$\pm$0.291 | 0.478$\pm$0.082 | 1.053$\pm$0.200 | 0.441$\pm$0.076 | 1.702$\pm$0.462 | 0.789$\pm$0.224 | 1.428$\pm$0.563 | 0.776$\pm$0.229
COTREC | 1.681$\pm$0.837 | 0.434$\pm$0.229 | 1.078$\pm$0.583 | 0.393$\pm$0.213 | 0.833$\pm$0.417 | 0.256$\pm$0.199 | 1.021$\pm$0.583 | 0.269$\pm$0.222
NirGNN(Ours) | 2.420$\pm$0.039 | 0.599$\pm$0.017 | 1.578$\pm$0.056 | 0.543$\pm$0.018 | 1.817$\pm$0.087 | 1.092$\pm$0.017 | 0.779$\pm$0.043 | 0.299$\pm$0.049

We run NirGNN on an NVIDIA GeForce RTX 3090 24G GPU for experiments. In the
following section, we will introduce the description of the dataset, and
present the experiment results of NirGNN for the new item recommendation,
ablation study, and parameter analysis on these datasets.

###  5.1. Dataset Description

####  5.1.1. Datasets.

The session-based user-oriented new item recommendation problem is a brand new
problem and there are no public datasets suitable for it yet. Thus, we
construct two real-world datasets to evaluate our model, including Amazon
Grocery and Gourmet Food (Amazon G&GF) dataset and Yelpsmall dataset.

  * •

Amazon G&GF. The Amazon G&GF dataset is the subset of the Amazon dataset
released in 2018111http://jmcauley.ucsd.edu/data/amazon/. Item attributes are
obtained from item data, including brands and prices. The Amazon dataset
describes the merchant with taxonomy, which uses a hierarchical taxonomy
relationship, such as [‘Sports & Outdoors’, ‘Other Sports’, ‘Dance’]. These
taxonomy relationships can be obtained directly.

  * •

Yelpsmall. The Yelpsmall dataset is from the 2021 release version of Yelp
stored on the Kaggle222https://www.kaggle.com/datasets/yelp-dataset/yelp-
dataset?datasetId=10100. The Yelpsmall is created by randomly selecting 6000
users from the Yelp dataset with the review time in 2016. Based on the comment
data of the two datasets, we obtained the session of user transactions and
merchants sorted by time. The taxonomy in the Yelp dataset does not contain a
hierarchical relationship. We use the following steps to generate the
corresponding taxonomy tree. First, we pre-train the GoogleNews-vectors-
negative300 by word2vec (mikolov2013efficient, ) to obtain the word vectors of
each taxonomy and then use K-Means++(arthur2006k, ) to cluster word vectors.
The same taxonomy’s word vectors are averaged before being clustered once
more. We first clustered $100$ taxonomy and then added the vectors of each
taxonomy. These $100$-word vectors are clustered into $50$ taxonomy and then
clustered into $10$ taxonomy, thus the three-level taxonomy tree is obtained.
A three-level taxonomy tree is created after three clustering iterations.

Based on the comment data of the two datasets, we obtained the session of user
transactions and merchants sorted by time. In a session, a new item is defined
as an item that has not appeared in the session. Therefore, we take the last
item visited by the user according to timestamp as the ground truth for the
new item recommendation, and delete it from the session to ensure that it is
unique and new to the user. Following the data division method of SR-GNN, the
session with a timestamp before $7$ days is divided into a training set, and
the session after $7$ days is the test set. Table 1 shows the details.

####  5.1.2. Baselines.

To evaluate the performance of the proposed method, we compare NirGNN with the
following state-of-the-art baselines. For baselines, we use new items to
calculate recommendation scores.

  * •

SR-GNN (wu2019session, ) is the first GNN-based model for the session-based
recommendation. It uses gated GNN to encode items in each session and employs
the attention mechanism to encode the global session preference.

  * •

SR-GNN-ATT is SR-GNN with an attention mechanism.

  * •

GC-SAN (xu2019graph, ) uses a gated GNN to encode items in each session and
uses multi-layer self-attention with residual connection to encode the global
session preference.

  * •

GCE-GNN (wang2020global, ) uses a global session graph and a local session
graph with position embedding.

  * •

COTREC (xia2021self, ) is a self-supervised graph co-training framework.
COTREC reports that the effect of COTREC is higher than that of the supervised
SOTA session-based GNN model.

####  5.1.3. Metrics.

The following metrics are used to evaluate the comparative methods. $P@k$
(Precision@$k$) is widely used as a measure of prediction accuracy. It
predicts the correct correlation result as a proportion of all returned
results. $k$ represents the precision of the first $k$ items in the list.
$MRR@k$ (Mean Reciprocal Rank) is the reciprocal rating of the re-averaged
correctly recommended item. A large $P$ value or a large $MRR$ value indicates
that the correct recommendation is at the top of the ranking list. In our
experiments, we take the $k=\\{10,20\\}$ as the evaluation index respectively.
We use percentages to represent these data.

Table 3. Ablation study on Amazon G&GF.

Methods | P@20 | MRR@20 | P@10 | MRR@10
---|---|---|---|---
NirGNN | 2.420$\pm$0.039 | 0.599$\pm$0.017 |  1.578$\pm$0.056 | 0.543$\pm$0.018
NirGNN w/o $\alpha$ intent |  2.410$\pm$0.030 |  0.596$\pm$0.021 |  1.508$\pm$0.048 |  0.538$\pm$0.014
NirGNN w/o $\beta$ intent |  2.375$\pm$0.019 |  0.589$\pm$0.018 |  1.512$\pm$0.008 |  0.538$\pm$0.009
NirGNN w/o $L_{zero}$ |  2.237$\pm$0.119 |  0.598$\pm$0.009 | 1.599$\pm$0.005 |  0.541$\pm$0.010

###  5.2. Evaluation Results

####  5.2.1. Overall Performance.

To demonstrate the overall performance of the proposed model, we compare it
with five state-of-the-art session-based GNN recommendation methods. Here, we
show new results on our proposed NirGNN framework along with baseline methods
for different metrics. We show experimental results on Amazon G&GF and
Yelpsmall datasets in Table 2, where the optimal solution for each metric is
highlighted in bold and ‘$\pm$’ indicates a numerical range. We observe that
NirGNN achieves significant performance gains under different metrics compared
to the baseline. In particular, on the Amazon G&GF dataset, in the case of
$k=20$, NirGNN can improve by up to $19.33\%$ on $P$ metrics and improve
$3.28\%$ on $MRR$ metrics which is compared to the second-best baseline. In
the case of $k=10$, NirGNN can improve by up to $22.52\%$ on $P$ metrics and
$2.65$ on $MRR$ metrics. On the Yelpsmall dataset, in the case of $k=20$,
NirGNN can be improved by up to $38.40\%$ on $MRR$ metrics, and improved by up
to $6.75\%$ on $P$ metrics compared to the second-best baseline. These
experimental results demonstrate that the NirGNN model can effectively improve
the learning performance for the new item recommendation.

(a) $\lambda$. (b) $\gamma$.

Figure 5. Parametric sensitivity on $\lambda$ and $\gamma$. Table 4.
Infomation of a session in Meituan dataset.

Item | A historical session | Candidate new items
---|---|---
1 | 2 | 3 | 4 | 5 | 4 | 6 | 7 | 8
t1 | Gourmet | Fruit | Gourment | Drinks | Gourmet | Drinks | Gourmet | Gourmet | Gourmet
t2 | Fast food | Packaged fruit | Chinese Cuisine | Milk Tea | Fast food | Milk Tea | Fast food | Fast food | Chinese cuisine
t3 | Hamburger | Unknown | Northwestern Cuisine | Unknown | Porridge | Unknown | Donburi | Meal packages | Beijing cuisine
a1 | 1 | 3 | 4 | 2 | 5 | 2 | 6 | 1 | 1
a2 | Merchant | Company | Company | Company | Company | Company | Merchant | Company | Company

Figure 6. A commercial case study on the Meituan dataset.

####  5.2.2. Ablation Study.

The ablation study is conducted on the Amazon G&GF dataset. To clarify the key
component of NirGNN, we report the ablation study of the $\alpha$ intent,
$\beta$ intent, and the $L_{zero}$ in Table 3. NirGNN w/o $\alpha$ means that
NirGNN is without user $\alpha$ intent and only with user $\beta$ intent.
NirGNN w/o $\beta$ means that NirGNN is without user $\beta$ intent and only
with user $\alpha$ intent. NirGNN w/o $L_{zero}$ means that NirGNN is without
$L_{zero}$ and only with $L_{ce}$ Loss. The experimental setup is the same as
the overall experiment. The results show that our NirGNN model is improved
after combining all key components. This reveals the effectiveness of each
component.

####  5.2.3. Parameter Sensitivity.

Parameter sensitivity analysis experiments are conducted on the Amazon G&GF
dataset. There are two key hyperparameters that should be tuned in our
objective function. The first is $\lambda$, which is used to balance $\alpha$
intent and $\beta$ intent in the overall intent $I$. Another hyperparameter is
$\gamma$, which is used to balance the cross-entropy loss and $L_{zero}$. By
varying $\lambda$ and $\gamma$ from 0.1 to 0.9 in 0.2 intervals, the
corresponding hyperparameter sensitivity analysis under $P@20$ metrics is
shown in Fig. 5. We find that the effect of using a combination of $\alpha$
intent and $\beta$ intent has fluctuated, but is efficacious. The proposed
NirGNN model performs best when $\gamma=0.3$. Although the two intents under
the $\lambda$ parameter appear to be conflicting, our ablation experiments
demonstrate that in the case of only $\alpha$ or only $\beta$ intent, the
effect is not as good as the combination of the two. So they don’t cancel each
other out.

###  5.3. Case Study

We provide a case study in a real recommendation scenario to demonstrate the
interpretability and utility of our model NirGNN. We take a private commercial
dataset provided by a food takeaway company, Meituan, as an example to
demonstrate the interpretability of the NirGNN model. The dataset is generated
by the food orders of Meituan within 30 days in a certain region. The dataset
includes a sequence of users buying takeaway food items, a three-level
taxonomy tree for each item, and two attributes for each item. We use the
dataset provided by Meituan to train the model and randomly select a session
from the Meituan dataset to explain our model. Table 4 shows information about
the case session in the Meituan dataset. The item row shows the ID of each
item in this session. $item_{1}$, $item_{2}$, $item_{3}$, $item_{4}$,
$item_{5}$ are the takeaway item that the user has visited in the history.
$item_{6}$, $item_{7}$, $item_{8}$ are candidate new items, where $item_{7}$
is the ground truth. $t_{1},t_{2},t_{3}$ represent the three-level taxonomy
tree, and we show the ID of each taxonomy. $a_{1},a_{2}$ represent the two
natural attributes of each takeaway food, including the brand ID and delivery
type of the takeaway food. Delivery types include professional delivery
(Company) provided by Meituan, and delivery provided by merchants (Merchant).
We provide one session for the case study. This session is
$item_{1}\rightarrow item_{2}\rightarrow item{3}\rightarrow
item_{4}\rightarrow item_{5}\rightarrow item_{4}$. A user’s purchase session
is shown in Fig. 6 (a), where $item_{1}$ is the starting item and $item_{4}$
is the ending item. We introduce the interpretability of NirGNN in terms of
the interpretability of user intent and the interpretability of reasoning
respectively.

####  5.3.1. Intent interpretability.

Fig. 6 (a) shows the interpretability of the user dual-intent. In the figure,
Each item contains a dual-intent score. We find that $item_{5}$ has the
highest score. Intuitively, $item_{4}$ is more in accordance with the user’s
preferences than others because it is purchased by the user twice recently.
However, our intent scores show that the score of $item_{4}$ is lower than
$item_{5}$. We observe from Table 4 that the data of $item_{5}$ is closer to
the ground truth $item_{7}$ than $item_{4}$, which highlights the
effectiveness of learning user dual-intent. In addition, Fig. 6 (b) shows the
interpretability of the recommendation process. After we learn the user’s
total intent, the total intent and each candidate new item are used to
calculate the matching score for a recommendation. The highest score generated
by new items multiplied by user intent will be recommended. We find that the
candidate new item $item_{7}$ has the highest score than others under the same
intent, so it is considered as the recommended item. The above-mentioned
process illustrates the interpretability of the NirGNN recommendation process.

####  5.3.2. New item reasoning.

Fig. 6 (c) shows the interpretability of new item embedding reasoning. We put
candidate items into the session and obtain their embedding via a GNN encoder.
Then we compare the embedding $\theta(atr^{*}_{7})$ generated by the
attributes of $item_{7}$ with the embeddings of other candidate items. We use
the Bhattacharyya distance to measure them. We find that the distance between
$\theta(atr^{*}_{7})$ between $item_{7}$ embedding is smaller than the
distance between $item_{6}$ or $item_{8}$, which confirms the interpretability
of new item reasoning.

##  6\. Conclusion

In this paper, we propose a novel GNN-based model, NirGNN, for the GSNIR
problem. To effectively learn user intent, we design a dual intent learning
network from both the attention perspective and data distribution perspective.
At the same time, to solve the problem that the new item has no interaction
with the user, we design a zero-shot-based new item reasoning method, which
reasons unknown new item embeddings by using attributes of new items.
Experimental results show that NirGNN is superior to SOTA methods. A real-
world case study shows that NirGNN is interpretable and can be applied in the
real world.

###### Acknowledgements.

This work is supported by the Natural Science Foundation of China under grants
62272340, 62276006 and Meituan Project.

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