Xingyi Zhang
ABSTRACT
In online social networks, users with similar interests tend to come together, forming social communities. Nowadays, user-defined communities become a prominent part of online social platforms as people who have joined such communities tend to be more active in social networks. Therefore, recommending explicit communities to users provides great potential to advance online services.
In this paper, we focus on the constrained social community recommendation problem in real applications, where each user can only join at most one community. Previous attempts at community recommendation mostly adopt collaborative filtering approaches or random walk-based approaches, while ignoring social relationships between users as well as the local structure of each community. Therefore, they only derive an extremely sparse affinity matrix, which degrades the model performances. To tackle this issue, we propose ComRec which simultaneously captures both global and local information on the extended graph during pre-computation, speeding up the training process on real-world large graphs. In addition, we present a labeling component to improve the expressiveness of our framework. We conduct experiments on three Tencent mobile games to evaluate our proposed method. Extensive experimental results show that our ComRec consistently outperforms other competitors by up to 12.80% and 6.61% in the corresponding evaluation metrics of offline and online experiments, respectively.
Supplemental Material
- Emily Alsentzer, Samuel Finlayson, Michelle Li, and Marinka Zitnik. 2020. Subgraph neural networks. In NeurIPS. 8017--8029.Google Scholar
- Aris Anagnostopoulos, Ravi Kumar, and Mohammad Mahdian. 2008. Influence and correlation in social networks. In SIGKDD. 7--15.Google Scholar
- Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. 2006. Group Formation in Large Social Networks: Membership, Growth, and Evolution. In SIGKDD. 44--54.Google Scholar
- Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020. Scaling Graph Neural Networks with Approximate PageRank. In SIGKDD. 2464--2473.Google Scholar
- Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018. Attentive Group Recommendation. In SIGIR. 645--654.Google Scholar
- Wen-Yen Chen, Dong Zhang, and Edward Y. Chang. 2008. Combinational Collaborative Filtering for Personalized Community Recommendation. In SIGKDD. 115--123.Google Scholar
- Xinyu Du, Xingyi Zhang, Sibo Wang, and ZengFeng Huang. 2023. Efficient Tree-SVD for Subset Node Embedding over Large Dynamic Graphs. PACMMOD, Vol. 1, 1 (2023), 96:1--96:26.Google ScholarDigital Library
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW. 417--426.Google Scholar
- Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In ICML. 1263--1272.Google ScholarDigital Library
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. 855--864.Google Scholar
- William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS.Google Scholar
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. 639--648.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
- Guanhao Hou, Xingguang Chen, Sibo Wang, and Zhewei Wei. 2021. Massively parallel algorithms for personalized pagerank. PVLDB, Vol. 14, 9 (2021), 1668--1680.Google ScholarDigital Library
- Guanhao Hou, Qintian Guo, Fangyuan Zhang, Sibo Wang, and Zhewei Wei. 2023. Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme. PACMMOD, Vol. 1, 1 (2023), 25:1--25:26.Google ScholarDigital Library
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In CVPR. 4700--4708.Google Scholar
- Shixun Huang, Wenqing Lin, Zhifeng Bao, and Jiachen Sun. 2022. Influence Maximization in Real-World Closed Social Networks. PVLDB, Vol. 16, 2 (2022), 180--192.Google ScholarDigital Library
- 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
- Wenqing Lin. 2019. Distributed algorithms for fully personalized pagerank on large graphs. In WWW. 1084--1094.Google Scholar
- Wenqing Lin. 2021. Large-Scale Network Embedding in Apache Spark. In SIGKDD. 3271--3279.Google Scholar
- Wenqing Lin, Feng He, Faqiang Zhang, Xu Cheng, and Hongyun Cai. 2020. Initialization for Network Embedding: A Graph Partition Approach. In WSDM. 367--374.Google Scholar
- Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu. 2022. Federated social recommendation with graph neural network. TIST, Vol. 13, 4 (2022), 1--24.Google Scholar
- Siqiang Luo, Xiaokui Xiao, Wenqing Lin, and Ben Kao. 2019. Efficient Batch One-Hop Personalized PageRanks. In ICDE. 1562--1565.Google Scholar
- Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology (2001), 415--444.Google Scholar
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: bringing order to the web. (1999).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
- Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao Yang, and Hari Sundaram. 2020. Groupim: A mutual information maximization framework for neural group recommendation. In SIGIR. 1279--1288.Google ScholarDigital Library
- Amit Sharma and Baoshi Yan. 2013. Pairwise Learning in Recommendation: Experiments with Community Recommendation on Linkedin. In RecSys. 193--200.Google Scholar
- Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. Autoint: Automatic feature interaction learning via self-attentive neural networks. In CIKM. 1161--1170.Google ScholarDigital Library
- Ellen Spertus, Mehran Sahami, and Orkut Buyukkokten. 2005. Evaluating similarity measures: a large-scale study in the orkut social network. In SIGKDD. 678--684.Google Scholar
- Diane Tang, Ashish Agarwal, Deirdre O'Brien, and Mike Meyer. 2010. Overlapping experiment infrastructure: More, better, faster experimentation. In SIGKDD. 17--26.Google Scholar
- Jiliang Tang, Charu Aggarwal, and Huan Liu. 2016. Recommendations in signed social networks. In WWW. 31--40.Google Scholar
- Jiliang Tang, Xia Hu, and Huan Liu. 2013. Social recommendation: a review. SNAM, Vol. 3, 4 (2013), 1113--1133.Google Scholar
- Vishvas Vasuki, Nagarajan Natarajan, Zhengdong Lu, and Inderjit S. Dhillon. 2010. Affiliation Recommendation Using Auxiliary Networks. In RecSys. 103--110.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In NeurIPS. 6000--6010.Google Scholar
- Hanzhi Wang, Mingguo He, Zhewei Wei, Sibo Wang, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen. 2021. Approximate graph propagation. In SIGKDD. 1686--1696.Google Scholar
- Hanzhi Wang, Zhewei Wei, Junhao Gan, Sibo Wang, and Zengfeng Huang. 2020. Personalized pagerank to a target node, revisited. In SIGKDD. 657--667.Google Scholar
- Sibo Wang, Youze Tang, Xiaokui Xiao, Yin Yang, and Zengxiang Li. 2016b. Hubppr: effective indexing for approximate personalized pagerank. PVLDB, Vol. 10, 3 (2016), 205--216.Google ScholarDigital Library
- Sibo Wang and Yufei Tao. 2018. Efficient algorithms for finding approximate heavy hitters in personalized pageranks. In SIGMOD. 1113--1127.Google Scholar
- Sibo Wang, Renchi Yang, Runhui Wang, Xiaokui Xiao, Zhewei Wei, Wenqing Lin, Yin Yang, and Nan Tang. 2019b. Efficient algorithms for approximate single-source personalized pagerank queries. TODS, Vol. 44, 4 (2019), 1--37.Google ScholarDigital Library
- Sibo Wang, Renchi Yang, Xiaokui Xiao, Zhewei Wei, and Yin Yang. 2017. FORA: Simple and Effective Approximate Single-Source Personalized PageRank. In SIGKDD. 505--514.Google ScholarDigital Library
- Xin Wang, Roger Donaldson, Christopher Nell, Peter Gorniak, Martin Ester, and Jiajun Bu. 2016a. Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization. In AAAI. 1331--1337.Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019a. Neural Graph Collaborative Filtering. In SIGIR. 165--174.Google Scholar
- Xiyuan Wang and Muhan Zhang. 2022. GLASS: GNN with Labeling Tricks for Subgraph Representation Learning. In ICLR.Google Scholar
- Zhewei Wei, Xiaodong He, Xiaokui Xiao, Sibo Wang, Shuo Shang, and Ji-Rong Wen. 2018. Topppr: top-k personalized pagerank queries with precision guarantees on large graphs. In SIGMOD. 441--456.Google Scholar
- Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.Google Scholar
- Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. 2020. Diffnet: A neural influence and interest diffusion network for social recommendation. TKDE (2020).Google Scholar
- Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019. A neural influence diffusion model for social recommendation. In SIGIR. 235--244.Google Scholar
- Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks. In ICLR.Google Scholar
- Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, and Sourav S. Bhowmick. 2020. Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank. PVLDB, Vol. 13, 5 (2020), 670--683.Google ScholarDigital Library
- Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019a. Social influence-based group representation learning for group recommendation. In ICDE. 566--577.Google Scholar
- Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019b. Social influence-based group representation learning for group recommendation. In ICDE. 566--577.Google Scholar
- Yuan Yin and Zhewei Wei. 2019. Scalable Graph Embeddings via Sparse Transpose Proximities. In SIGKDD. 1429--1437.Google Scholar
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018a. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In SIGKDD. 974--983.Google Scholar
- Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018b. Hierarchical graph representation learning with differentiable pooling. NeurIPS, Vol. 31 (2018).Google Scholar
- Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In AAAI, Vol. 32.Google ScholarCross Ref
- Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021a. Labeling trick: A theory of using graph neural networks for multi-node representation learning. NeurIPS, Vol. 34 (2021), 9061--9073.Google Scholar
- Shiqi Zhang, Jiachen Sun, Wenqing Lin, Xiaokui Xiao, and Bo Tang. 2022. Measuring Friendship Closeness: A Perspective of Social Identity Theory. In CIKM. 3664--3673.Google Scholar
- Xingyi Zhang, Kun Xie, Sibo Wang, and Zengfeng Huang. 2021b. Learning Based Proximity Matrix Factorization for Node Embedding. In SIGKDD. 2243--2253.Google Scholar
Index Terms
- Constrained Social Community Recommendation
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