ABSTRACT
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance.
In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train, exhibiting substantial improvements (about 16.0% relative improvement on average) over Neural Graph Collaborative Filtering (NGCF) -- a state-of-the-art GCN-based recommender model -- under exactly the same experimental setting. Further analyses are provided towards the rationality of the simple LightGCN from both analytical and empirical perspectives.
- Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR.Google Scholar
- Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, and Yi-Hsuan Yang. 2019 b. Collaborative Similarity Embedding for Recommender Systems. In WWW. 2637--2643.Google Scholar
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. In SIGIR. 335--344.Google Scholar
- Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In AAAI.Google Scholar
- Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, and Yue Wang. 2019 a. (λ)Opt: Learn to Regularize Recommender Models in Finer Levels. In KDD. 978--986.Google Scholar
- Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan S. Kankanhalli. 2018. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. In WWW. 639--648.Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.Google Scholar
- Michaë l Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurIPS. 3837--3845.Google Scholar
- Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data. In IJCAI. 2230--2236.Google Scholar
- Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative Memory Network for Recommendation Systems. In SIGIR. 515--524.Google Scholar
- Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua. 2019. Temporal Relational Ranking for Stock Prediction. TOIS, Vol. 37, 2 (2019), 27:1--27:30.Google ScholarDigital Library
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256.Google Scholar
- Marco Gori and Augusto Pucci. 2007. ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. In IJCAI. 2766--2771.Google Scholar
- William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1025--1035.Google Scholar
- Taher H Haveliwala. 2002. Topic-sensitive pagerank. In WWW. 517--526.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.Google Scholar
- Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. In SIGIR. 355--364.Google Scholar
- Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural Attentive Item Similarity Model for Recommendation. TKDE, Vol. 30, 12 (2018), 2354--2366.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW. 173--182.Google Scholar
- Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, and Tat-Seng Chua. 2019. Fast Matrix Factorization with Nonuniform Weights on Missing Data. TNNLS (2019).Google Scholar
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: factored item similarity models for top-N recommender systems. In KDD. 659--667.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.Google Scholar
- Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. 426--434.Google Scholar
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, Vol. 42, 8 (2009), 30--37.Google ScholarDigital Library
- Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI. 3538--3545.Google Scholar
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In WWW. 689--698.Google Scholar
- Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. DeepInf: Social Influence Prediction with Deep Learning. In KDD. 2110--2119.Google Scholar
- Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, and Inderjit S Dhillon. 2015. Collaborative filtering with graph information: Consistency and scalable methods. In NIPS. 2107--2115.Google Scholar
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In WSDM. 273--282.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. 452--461.Google ScholarDigital Library
- Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. Fast context-aware recommendations with factorization machines. In SIGIR. 635--644.Google Scholar
- Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In WWW. 729--739.Google Scholar
- Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2018. Graph Convolutional Matrix Completion. In KDD Workshop on Deep Learning Day.Google Scholar
- Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.Google Scholar
- Jun Wang, Arjen P. de Vries, and Marcel J. T. Reinders. 2006. Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion. In SIGIR. 501--508.Google Scholar
- Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019 a. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD. 950--958.Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 b. Neural Graph Collaborative Filtering. In SIGIR. 165--174.Google Scholar
- Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019 a. Simplifying Graph Convolutional Networks. In ICML. 6861--6871.Google Scholar
- Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019 b. A Neural Influence Diffusion Model for Social Recommendation. In SIGIR. 235--244.Google Scholar
- Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks?. In ICLR.Google Scholar
- Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-rec: high-order proximity for implicit recommendation. In RecSys. 140--144.Google Scholar
- Yinwei Yin, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multimodal Graph Convolution Network for Personalized Recommendation of Micro-video. In MM.Google Scholar
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In KDD (Data Science track). 974--983.Google Scholar
- Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang. 2020. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. In SIGIR.Google Scholar
- Cheng Zhao, Chenliang Li, and Cong Fu. 2019. Cross-Domain Recommendation via Preference Propagation GraphNet. In CIKM. 2165--2168.Google Scholar
- Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, and Yongdong Zhang. 2020. Bilinear Graph Neural Network with Neighbor Interactions. In IJCAI.Google Scholar
Index Terms
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Recommendations
Graph Trend Filtering Networks for Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalRecommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online ...
Neural graph personalized ranking for Top-N Recommendation
AbstractPersonalized recommendation has been widely applied to many real-world services. Many of recent studies focus on collaborative filtering (CF) by deep neural networks, which pursue to predict users’ preference on items based on the past ...
Neural Graph Collaborative Filtering
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information RetrievalLearning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or ...
Comments