Sequential Recommendation with Graph Neural Networks

By Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, Yong Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

482 citations recorded by the source corpus.

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often implicit and noisy preference signals, they cannot sufficiently reflect users' actual preferences. In addition, users' dynamic preferences often change rapidly over time, and hence it is difficult to capture user patterns in their historical sequences. In this work, we propose a graph neural network model called SURGE (short forSeqUential Recommendation with Graph neural nEtworks) to address these two issues. Specifically, SURGE integrates different types of preferences in long-term user behaviors into clusters in the graph by re-constructing loose item sequences into tight item-item interest graphs based on metric learning. This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph. Then, we perform cluster-aware and query-aware graph convolutional propagation and graph pooling on the constructed graph. It dynamically fuses and extracts users' current activated core interests from noisy user behavior sequences. We conduct extensive experiments on both public and proprietary industrial datasets. Experimental results demonstrate significant performance gains of our proposed method compared to state-of-the-art methods. Further studies on sequence length confirm that our method can model long behavioral sequences effectively and efficiently.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fbb87e32fd63ae980c0ff6cf6878fd5e4d7bda1d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Chen Gao
5. Chao Huang
6. Lianghao Xia
7. Depeng Jin
8. Dawei Yin

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Chen Gao
5. Chao Huang
6. Lianghao Xia
7. Depeng Jin
8. Dawei Yin

In Management Science and Operations Research

1. Xiang Wang
2. Xiangnan He
3. Fei Sun
4. Bin Cui
5. Lianghao Xia
6. Chao Huang
7. Shiwen Wu
8. Fei Sun

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “…AAAI 2020 STP-UDGAT[102] CIKM 2020 GPR[13] CIKM 2020 GES-SASRec[253] TKDE 2021 RetaGNN[69] WWW 2021 TGSRec[44] CIKM 2021 SGRec[97] IJCAI 2021 SURGE [15] SIGIR 2021 Session SR-GNN [203] AAAI 2019 GC-SAN [215] IJCAI 2019 TA-GNN [224] SIGIR 2020 MGNN-SPred [177] WWW 2020 LESSR [24] KDD 2020 MKM-SR…”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “Unlike GGNN which treats the neighbors equally, attention mechanism is also utilized to differentiate the importance of neighbors [12, 115, 163].”
• Representation Learning with Large Language Models for Recommendation (2023, background): “Recommender systems have evolved to provide personalized item recommendations based on user interactions, with deep learning and graph neural networks playing a significant role [4, 39].”
• Graph Neural Networks for Recommender System (2022, background): “The scenario consists of social [24], sequential [4], session-”