How graph convolutions amplify popularity bias for recommendation?

Abstract

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias — tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of recommender systems in the long run.

In this paper, we investigate how graph convolutions amplify the popularity bias in RS. Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (i.e., neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential. The two points make popular items get closer to almost users and thus being recommended more frequently. To rectify this, we propose to estimate the amplified effect of popular nodes on each node’s representation, and intervene the effect after each graph convolution. Specifically, we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node, then remove the effect from the node embeddings at each graph convolution layer. Our method is simple and generic — it can be used in the inference stage to correct existing models rather than training a new model from scratch, and can be applied to various GCN models. We demonstrate our method on two representative GCN backbones LightGCN and UltraGCN, verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular items. Codes are open-sourced ^†

See github.com/MEICRS/DAP website.

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Graphical abstract

Keywords

recommendation / graph convolution networks / popularity bias

Jiajia Chen is currently working towards the PhD degree at University of Science and Technology of China (USTC), China. His research interests include recommender systems and graph learning

Jiancan Wu is a postdoctoral researcher at the University of Science and Technology of China (USTC), China, where he completed his BE and PhD. His research interests focus on information retrieval, data mining, self-supervised learning, particularly in recommender systems, graph learning. He has published several conference papers in top-tier venues such as SIGIR, WWW, and KDD. Moreover, he has served as the PC member for several top conferences including SIGIR, WWW, KDD, AAAI, etc., and the regular reviewer for journals including TKDE, TOIS, etc

Jiawei Chen is a Research Fellow in School of Computer Science and Technology, Zhejiang University, China. His research interests include information retrieval, data mining, and causal reasoning. He received PhD degree in computer science from Zhejiang University, China in 2020. He has published over 20 academic papers on top-tier conferences or journals such as WWW, SIGIR, AAAI, KDD, TOIS, and TKDE

Xin Xin is now a tenure-track assistant professor in the School of Computer Science and Technology of Shandong University, China as a member of the Information Retrieval Lab. Before that, he got his PhD degree in computing science from University of Glasgow, UK. His research interests span recommender systems, reinforcement learning, graph learning, causual inference for recommendation and NLP. His work appeared in sereval top-tier ML & IR conferences including SIGIR, WSDM, WWW, etc. He is also a (senior) program commitee member of leading academic conferences and journals, such as SIGIR, KDD, TOIS, etc

Yong Li is currently a tenured associate professor of the Department of Electronic Engineering, Tsinghua University, China. He received the PhD degree in electronic engineering from Tsinghua University, China in 2012. His research interests include city science and urban computing. Dr. Li has served as General Chair, TPC Chair, SPC/TPC Member for several international workshops and conferences, and he is on the editorial board of two IEEE journals. He has published over 100 papers on first-tier international conferences and journals, including Nature Comp. Sci., KDD, NeurIPS, WWW, UbiComp, and his papers have total citations more than 13000. Among them, ten are ESI Highly Cited Papers in Computer Science, and five receive conference Best Paper (run-up) Awards. He received IEEE 2016 ComSoc Asia-Pacific Outstanding Young Researchers, Young Talent Program of China Association for Science and Technology, and the National Youth Talent Support Program

Xiangnan He is a professor at the University of Science and Technology of China (USTC), China. His research interests span information retrieval, data mining, and multi-media analytics. He has over 100 publications that appeared in top conferences such as SIGIR, WWW, and KDD, and journals including TKDE, and TOIS. His work has received the Best Paper Award Honorable Mention in SIGIR (2021, 2016) and WWW (2018). He is in the editorial board for several journals including ACM Transactions on Information Systems (TOIS), IEEE Transactions on Big Data (TBD), AI Open, etc

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Acknowledgements

This work was supported by the National Key R&D Program of China (2021ZD0111802), the National Natural Science Foundation of China (Grant No. 19A2079), and the CCCD Key Lab of Ministry of Culture and Tourism.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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