ABSTRACT
This paper proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis---social influence and latent interests---to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative Recall@K) over state-of-the-art baselines across multiple publicly available datasets.
- Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems. 3844--3852.Google Scholar
- Tyler Derr, Yao Ma, and Jiliang Tang. 2018. Signed graph convolutional networks. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 929--934.Google ScholarCross Ref
- Shanshan Feng, Gao Cong, Arijit Khan, Xiucheng Li, Yong Liu, and Yeow Meng Chee. 2018. Inf2vec: Latent Representation Model for Social Influence Embedding. In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 941--952.Google ScholarCross Ref
- David Fullagar, Christopher Newton, and Laurence R Lipstone. 2015. Handling long-tail content in a content delivery network (CDN). US Patent 8,930,538.Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 173--182.Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 549--558.Google ScholarDigital Library
- Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, 193--201.Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 135--142.Google ScholarDigital Library
- Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, and Shiqiang Yang. 2012. Social contextual recommendation. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 45--54.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).Google Scholar
- Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).Google Scholar
- Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram. 2018b. An Adversarial Approach to Improve Long-Tail Performance in Neural Collaborative Filtering. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 1491--1494.Google ScholarDigital Library
- Adit Krishnan, Ashish Sharma, and Hari Sundaram. 2018a. Insights from the Long-Tail: Learning Latent Representations of Online User Behavior in the Presence of Skew and Sparsity. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 297--306.Google ScholarDigital Library
- Brian Kulis et almbox. 2013. Metric learning: A survey. Foundations and Trends® in Machine Learning , Vol. 5, 4 (2013), 287--364.Google Scholar
- Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 831--840.Google ScholarDigital Library
- Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016a. Modeling user exposure in recommendation. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 951--961.Google ScholarDigital Library
- Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016b. Modeling user exposure in recommendation. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 951--961.Google ScholarDigital Library
- Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 689--698.Google ScholarDigital Library
- Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 931--940.Google ScholarDigital Library
- Benjamin M Marlin. 2004. Modeling user rating profiles for collaborative filtering. In Advances in neural information processing systems. 627--634.Google Scholar
- Peter V Marsden and Noah E Friedkin. 1993. Network studies of social influence. Sociological Methods & Research , Vol. 22, 1 (1993), 127--151.Google ScholarCross Ref
- Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology , Vol. 27, 1 (2001), 415--444.Google Scholar
- Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in neural information processing systems. 1257--1264.Google Scholar
- Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Twenty-fourth AAAI conference on artificial intelligence .Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452--461.Google ScholarDigital Library
- Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European Semantic Web Conference . Springer, 593--607.Google ScholarDigital Library
- Cosma Rohilla Shalizi and Andrew C Thomas. 2011. Homophily and contagion are generically confounded in observational social network studies. Sociological methods & research , Vol. 40, 2 (2011), 211--239.Google Scholar
- Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 729--739.Google ScholarDigital Library
- Petar Velivc ković , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).Google Scholar
- Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 515--524.Google ScholarDigital Library
- Menghan Wang, Xiaolin Zheng, Yang Yang, and Kun Zhang. 2018. Collaborative filtering with social exposure: A modular approach to social recommendation. In Thirty-Second AAAI Conference on Artificial Intelligence .Google Scholar
- Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning , Vol. 8, 3--4 (1992), 229--256.Google ScholarDigital Library
- Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, and Meng Wang. 2018. SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. arXiv preprint arXiv:1811.02815 (2018).Google Scholar
- Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019 a. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. arXiv preprint arXiv:1903.10433 (2019).Google Scholar
- Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019 b. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. arXiv preprint arXiv:1903.10433 (2019).Google Scholar
- Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 153--162.Google ScholarDigital Library
- Tong Zhao, Julian McAuley, and Irwin King. 2014. Leveraging social connections to improve personalized ranking for collaborative filtering. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, 261--270.Google ScholarDigital Library
Index Terms
- A Modular Adversarial Approach to Social Recommendation
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