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Graph Neural Networks for Recommender Systems:

Challenges, Methods, and Directions

CHEN GAO*

, YU ZHENG*

, NIAN LI

, YINFENG LI

, YINGRONG QIN

, JINGHUA

PIAO

, YUHAN QUAN

, JIANXIN CHANG

, DEPENG JIN

, XIANGNAN HE

, YONG

LI*

Beijing National Research Center for Information Science and Technology (BNRist), Department of

Electronic Engineering, Tsinghua University and

School of Information Science and Technology, University

of Science and Technology of China

Recommender system is one of the most important information services on today’s Internet. Recently, graph

neural networks have become the new state-of-the-art approach of recommender systems. In this survey,

we conduct a comprehensive review of the literature in graph neural network-based recommender systems.

We rst introduce the background and the history of the development of both recommender systems and

graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing

works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist

of two categories, spectral models and spatial ones. We then discuss the motivation of applying graph

neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural

property of data, and the enhanced supervision signal. We then systematically analyze the challenges in

graph construction, embedding propagation/aggregation, model optimization, and computation eciency.

Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph

neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions

on the open problems and promising future directions of this area. We summarize the representative papers

along with their codes repositories in https://github.com/tsinghua-b-lab/GNN-Recommender-Systems.

Additional Key Words and Phrases: Recommender Systems, Graph Neural Networks, Graph Representation

Learning; Information Retrieval

ACM Reference Format:

Chen Gao*

, Yu Zheng*

, Nian Li

, Yinfeng Li

, Yingrong Qin

, Jinghua Piao

, Yuhan Quan

, Jianxin Chang

Depeng Jin

, Xiangnan He

, Yong Li*

. 2021. Graph Neural Networks for Recommender Systems: Challenges,

Methods, and Directions. ACM Transactions on Information Systems 1, 1 (September 2021), 46 pages. https:

//doi.org/10.1145/nnnnnnn.nnnnnnn

1 INTRODUCTION

Recommender system, is a kind of ltering system of which the goal is to present personalized

information to users, which improves the user experience and promotes business prot. As one of

∗

Yong Li is the Corresponding Author. The rst two authors contributed equally to this paper.

Author’s address: Chen Gao*

, Yu Zheng*

, Nian Li

, Yinfeng Li

, Yingrong Qin

, Jinghua Piao

, Yuhan Quan

, Jianxin

Chang

, Depeng Jin

, Xiangnan He

, Yong Li*

Beijing National Research Center for Information Science and Technology

(BNRist), Department of Electronic Engineering, Tsinghua University,

School of Information Science and Technology,

University of Science and Technology of China, chgao96@gmail.com,{y-zheng19,linian21,liyf19,qyr16,pojh19,quanyh19,

changjx18}@mails.tsinghua.edu.cn,xiangnanhe@gmail.com,{jindp,liyong07}@tsinghua.edu.cn.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee

provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and

the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored.

Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires

prior specic permission and/or a fee. Request permissions from permissions@acm.org.

1046-8188/2021/9-ART $15.00

https://doi.org/10.1145/nnnnnnn.nnnnnnn

ACM Transactions on Information Systems, Vol. 1, No. 1, Article . Publication date: September 2021.

arXiv:2109.12843v1 [cs.IR] 27 Sep 2021

2 Gao et al.

the typical applications of machine learning driven by the real world, it is an extremely hot topic in

both industrial and academia nowadays.

To recap the history of recommender systems, it can be generally divided into three stages,

shallow models [

125

126

], neural models [

], and GNN-based models [

153

188

]. The

earliest recommendation models capture the collaborative ltering (CF) eect by directly calculating

the similarity of interactions. Then model-based CF methods, such as matrix factorization (MF) [

]

or factorization machine[

125

], were proposed to approach recommendation as a representation

learning problem. However, these methods are faced with critical challenges such as complex user

behaviors or data input. To address it, neural network-based models [

] are proposed. For

example, neural collaborative ltering (NCF) was developed to extend the inner product in MF

with multi-layer perceptrons (MLP) to improve its capacity. Similarly, deep factorization machine

(DeepFM) [

] combined the shallow model factorization machine (FM) [

125

] with MLP. However,

these methods are still highly limited since their paradigms of prediction and training ignore the

high-order structural information in observed data. For example, the optimization goal of NCF

is to predict user-item interaction, and the training samples include observed positive user-item

interactions and unobserved negative user-item interactions. It means that during the parameter

updating for a specic user, only the items interacted by him/her are involved.

Recently, the advances of graph neural networks provide a strong fundamental and opportunity

to address the above issues in recommender systems. Specically, graph neural networks adopt

embedding propagation to aggregate neighborhood embedding iteratively. By stacking the propaga-

tion layers, each node can access high-order neighbors’ information, rather than only the rst-order

neighbors’ as the traditional methods do. With its advantages to handle the structural data and

to explore structural information, GNN-based methods have become the new state-of-the-art

approaches in recommender systems.

To well apply graph neural networks into recommender systems, there are some critical chal-

lenges required to be addressed. First, the data input of recommender system should be carefully

and properly constructed to graph, with nodes representing elements and edges representing the

relations. Second, for the specic task, the component in the graph neural network should be

adaptively designed, including how to propagate and aggregate, in which existing works have

explored various choices with dierent advantages and disadvantages. Third, the optimization of

the GNN-based model, including the optimization goal, loss function, data sampling, etc., should

be consistent with the task requirement. Last, since recommender systems have strict limitations

on the computation cost, and also due to GNNs’ embedding propagation operations introduce

a number of computations, the ecient deployment of graph neural networks in recommender

systems is another critical challenge.

In this paper, we aim to provide a systematic and comprehensive review of the research eort,

especially on how they improve recommendation with graph neural networks and address the

corresponding challenges. To fulll a clear understanding, we categorize researches of recommender

systems from four perspectives, stage, scenario, objectives, and applications. We summarize the

representative papers along with their codes repositories in https://github.com/tsinghua-b-lab/

GNN-Recommender-Systems.

It is worth mentioning that there is one existing survey [

168

] of graph neural network-based

recommender system. However, it is limited due to the following reasons. First, it does not provide

extensive taxonomy of recommender systems. Specically, it roughly divides recommender systems

into non-sequential recommendation and sequential recommendation, however, which is not so

reasonable. In fact, the sequential recommendation is only one specic recommendation scenario

with a special setting of input and output, as pointed out by this survey. Second, it does not provide

adequate explanations of the motivations and reasons that the existing works leverage graph

ACM Transactions on Information Systems, Vol. 1, No. 1, Article . Publication date: September 2021.

Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 3

• Accuracy (precision)

• Diversity / Novelty

• Explainability / Interpretability

• Fairness

• Security and Privacy

Objective

Recommender System

• Matching (Collaborative Filtering)

• Ranking (Feature-based / CTR)

• Re-Ranking

Stage

• Social Recommendation

• Sequential Recommendation

• Session-based Recommendation

• Cross-domain Recommendation

• Multi-behavior Recommendation

• Bundle Recommendation

Application

• Product Recommendation

• POI Recommendation

• News Recommendation

• Movie Recommendation

• Video Recommendation

• Music Recommendation

Scenario

Fig. 1. An illustration of typical recommender systems (stages, scenarios, objectives, and applications)

neural networks for recommender systems. In this survey, by contrast, we provide a comprehensive

understanding of why GNN can be and should be used in recommender system, which can help

readers to understand the position and value of this new research eld. Third, it does not explain

the critical challenges of applying graph neural networks to recommendation and how to address

them, which are fully discussed in this survey. Last, since this area is increasingly popular, our

survey also introduces a lot of recently published papers not covered by [168].

The structure of this survey is organized as follows. We rst introduce the background of

recommender systems, from four kinds of perspectives (stage, scenario, objective, application), and

the background of graph neural networks, in Section 2. We then discuss the challenges of applying

graph neural networks to recommender systems from four aspects, in Section 3. Then we elaborate

on the representative methods of graph nerual network-based recommendation in Section 4 by

following the taxonomy in the above section. We discuss the most critical open problems in this

area and provide ideas of the future directions in Section 5 and conclude this survey in Section 6.

2 BACKGROUND

2.1 Recommender Systems

2.1.1 Overview . In this section, we present the background of recommender systems from four

perspectives: stages, scenarios, objectives, and applications. Specically, in industrial applications,

due to the real-world requirements on system engineering, the recommender systems are always

split into three stages, matching, ranking, and re-ranking, forming a standard pipeline. Each stage

has dierent characteristics on data input, output, model design, etc. Besides the standard stages,

there are many specic recommendation scenarios with a special denition. For example, in the

last twenty years, social recommendation has been attracting attention, dened as improving

recommender system based on social relations. Last, dierent recommender systems have dierent

objectives, of which accuracy is always the most important one as it directly determines the

system’s utility. Recently, recommender systems have been assigned other requirements such as

recommending diversied items to avoid boring user experience, making sure the system treats

all users fairly, protecting user privacy from attack, etc. As for the applications, GNN models can

ACM Transactions on Information Systems, Vol. 1, No. 1, Article . Publication date: September 2021.

4 Gao et al.

Stage Matching Ranking Re-ranking

Pipeline

Item Amount

Millions → Hundreds Hundreds → Tens Tens → Tens

Alias Candidate Generation, Retrieval Scoring, CTR prediction

Post

processing, Policy

Challenge Relevance, Efficiency

Accuracy, Feature Interaction

Item Relationship

item pool

Ranking

Model

Matching Source 1

Matching Source 2

Matching Source K

…

User Features

Item Features

Re-ranking

Model

Fig. 2. The typical pipeline of recommender systems.

be widely deployed in e-commerce recommendation, point-of-interest recommendation, news

recommendation, movie recommendation, music recommendation, etc.

2.1.2 Stages . The item pool, i.e. all the items available for recommender systems, is usually large

and can include millions of items. Thus, common recommender systems follow a multi-stage archi-

tecture, ltering items stage by stage from the large-scale item pool to the nal recommendations

exposed to users, tens of items. [

158

]. Generally, a modern recommender system is composed

of the following three stages.

• Matching.

This rst stage generates hundreds of candidate items from the extremely large

item pool (million-level or even billion-level), which signicantly reduces the scale. Considering

the large scale of data input in this stage, and due to the strict latency restrictions of online

serving, complicated algorithms cannot be adopted, such as very deep neural networks [

In other words, models in this stage are usually concise. That is, the core task of this stage is to

retrieve potentially relevant items with high eciency and attain coarse-grained modeling of

user interests. It is worth noting that a recommender system in the real world usually contains

multiple matching channels with multiple models, such as embedding matching, geographical

matching, popularity matching, social matching, etc.

• Ranking.

After the matching stage, multiple sources of candidate items from dierent channels

are merged into one list and then scored by a single ranking model. Specically, the ranking

model ranks these items according to the scores, and the top dozens of items are selected. Since

the amount of input items in this stage is relatively small, the system can aord much more

complicated algorithms to achieve higher recommendation accuracy [69, 89, 133]. For example,

rich features including user proles and item attributes can be taken into consideration, and

advanced techniques such as self-attention [

] can be utilized. Since a lot of features are involved,

the key challenge in this stage is to design appropriate models for capturing complicated feature

interactions.

• Re-ranking.

Although the obtained item list after the ranking stage is optimized with respect

to relevance, it may not meet other important requirements, such as freshness, diversity, fairness,

etc. [

120

] Therefore, a re-ranking stage is necessary, which usually removes certain items or

changes the order of the list in order to fulll additional criteria and also satisfy business needs.

The main concern in this stage is to consider multiple relationships among the top-scored items

[

217

]. For example, similar or substitutable items can lead to information redundancy when

they are displayed closely in the recommendations.

ACM Transactions on Information Systems, Vol. 1, No. 1, Article . Publication date: September 2021.

Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 5

User-item Interaction Social Networks

Fig. 3. An illustration of social recommendation. User interactions are aected by both their own preferences

and the social factor.

Sequence

Fig. 4. An illustration of sequential recommendation. Given a user’s historical sequence, recommender

system aims to predict the next item.

Fig. 2 illustrates the typical pipeline of a recommender system, as well as the comparisons of the

three stages above.

2.1.3 Scenarios. In the following, we will elaborate on the dierent scenarios of recommender

systems, including social recommendation, sequential recommendation, session recommendation,

bundle recommendation, cross-domain recommendation, and multi-behavior recommendation.

• Social Recommendation.

In the past few years, social platforms have dramatically changed

users’ daily life. With the ability to interact with other users, individual behaviors are driven by

both personal and social factors. Specically, users’ behavior may be inuenced by what their

friends might do or think, which is known as social inuence [

]. For example, users in WeChat

∗

Video platform may like some videos only because of their WeChat friends’ like behaviors. At

the same time, social homophily is another popular phenomenon in many social platforms, i.e.,

people tend to build social relations with others who have similar preferences with them [

107

Taking social e-commerce as an example, users from a common family possibly share similar

product preferences, such as food, clothes, daily necessities, and so on. Hence, social relations are

often integrated into recommender systems to enhance the nal performance, which is called

social recommendation. Fig. 3 illustrates the data input of social recommendation, of which user

interactions are determined by both his/her own preferences and social factors (social inuence

and social homophily).

• Sequential Recommendation.

In recommender systems, users will produce a large number of

interaction behaviors over time. The sequential recommendation method extracts information

from these behavioral sequences and predicts the user’s next interaction item, as shown in

Fig. 4. To symbolize this problem, for the sequence of items

{𝑥

, 𝑥

, ..., 𝑥

𝑛

}

that the user has

∗

http://wechat.com/

ACM Transactions on Information Systems, Vol. 1, No. 1, Article . Publication date: September 2021.

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