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Enhancing Personalized Recommendation by Implicit Preference Communities Modeling

Published: 26 November 2019 Publication History

Abstract

Recommender systems aim to capture user preferences and provide accurate recommendations to users accordingly. For each user, there usually exist others with similar preferences, and a collection of users may also have similar preferences with each other, thus forming a community. However, such communities may not necessarily be explicitly given, and the users inside the same communities may not know each other; they are formally defined and named Implicit Preference Communities (IPCs) in this article. By enriching user preferences with the information of other users in the communities, the performance of recommender systems can also be enhanced.
Historical explicit ratings are a good resource to construct the IPCs of users but is usually sparse. Meanwhile, user preferences are easily affected by their social connections, which can be jointly used for IPC modeling with the ratings. However, this imposes two challenges for model design. First, the rating and social domains are heterogeneous; thus, it is challenging to coordinate social information and rating behaviors for a same learning task. Therefore, transfer learning is a good strategy for IPC modeling. Second, the communities are not explicitly labeled, and existing supervised learning approaches do not fit the requirement of IPC modeling. As co-clustering is an effective unsupervised learning approach for discovering block structures in high-dimensional data, it is a cornerstone for discovering the structure of IPCs.
In this article, we propose a recommendation model with Implicit Preference Communities from user ratings and social connections. To tackle the unsupervised learning limitation, we design a Bayesian probabilistic graphical model to capture the IPC structure for recommendation. Meanwhile, following the spirit of transfer learning, both rating behaviors and social connections are introduced into the model by parameter sharing. Moreover, Gibbs sampling-based algorithms are proposed for parameter inferences of the models. Furthermore, to meet the need for online scenarios when the data arrive sequentially as a stream, a novel online sampling-based parameter inference algorithm for recommendation is proposed. To the best of our knowledge, this is the first attempt to propose and formally define the concept of IPC.

References

[1]
Jacob Abernethy, Kevin Canini, John Langford, and Alex Simma. 2007. Online collaborative filtering. University of California at Berkeley, Tech. Rep (2007).
[2]
Y. Bao, H. Fang, and J. Zhang. 2014. Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’14). 350.
[3]
Nicola Barbieri, Giuseppe Manco, and Ettore Ritacco. 2014. Probabilistic approaches to recommendations. Synth. Lect. Data Min. Knowl. Discov. 5, 2 (2014), 1--197.
[4]
Alex Beutel, Amr Ahmed, and Alexander J. Smola. 2015. ACCAMS: Additive co-clustering to approximate matrices succinctly. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 119--129.
[5]
Alex Beutel, Kenton Murray, Christos Faloutsos, and Alexander J. Smola. 2014. CoBaFi: Collaborative Bayesian filtering. In Proceedings of the 23rd International Conference on World Wide Web (WWW’14). ACM, New York, NY, USA, 97--108.
[6]
Ioan Buciu and Ioannis Pitas. 2004. Application of non-negative and local non negative matrix factorization to facial expression recognition. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Vol. 1. IEEE, 288--291.
[7]
Deng Cai, Xiaofei He, Xuanhui Wang, Hujun Bao, and Jiawei Han. 2009. Locality preserving nonnegative matrix factorization. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’09), Vol. 9. 1010--1015.
[8]
Da Cao, Xiangnan He, Liqiang Nie, Xiaochi Wei, Xia Hu, Shunxiang Wu, and Tat-Seng Chua. 2017. Cross-platform app recommendation by jointly modeling ratings and texts. ACM Trans. Inf. Syst. 35, 4, Article 37 (Jul. 2017), 27 pages.
[9]
Allison J. B. Chaney, David M. Blei, and Tina Eliassi-Rad. 2015. A probabilistic model for using social networks in personalized item recommendation. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 43--50.
[10]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191--198.
[11]
Inderjit S. Dhillon. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’01). ACM, New York, NY, 269--274.
[12]
Nan Du, Bin Wu, Xin Pei, Bai Wang, and Liutong Xu. 2007. Community detection in large-scale social networks. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. ACM, 16--25.
[13]
Santo Fortunato. 2010. Community detection in graphs. Phys. Rep. 486, 3 (2010), 75--174.
[14]
Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 123--129.
[15]
Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 355--364.
[16]
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.
[17]
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.
[18]
Patrik O. Hoyer. 2004. Non-negative matrix factorization with sparseness constraints. The Journal of Machine Learning Research 5 (2004), 1457--1469.
[19]
Shanshan Huang, Jun Ma, Peizhe Cheng, and Shuaiqiang Wang. 2015. A hybrid multigroup coclustering recommendation framework based on information fusion. ACM Trans. Intell. Syst. Technol. 6, 2, Article 27 (Mar. 2015), 22 pages.
[20]
Wenyi Huang, Zhaohui Wu, Liang Chen, Prasenjit Mitra, and C. Lee Giles. 2015. A neural probabilistic model for context based citation recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’15). 2404--2410.
[21]
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 (RecSys’10). 135--142.
[22]
Meng Jiang, Peng Cui, Fei Wang, Qiang Yang, Wenwu Zhu, and Shiqiang Yang. 2012. Social recommendation across multiple relational domains. In Proceedings of the Conference on Information and Knowledge Management (CIKM’12).
[23]
Yuan Jiang, Hechang Chen, and Bo Yang. 2018. Deep social collaborative filtering by trust. In Proceedings of 2018 International Conference on Big Data Technologies (ICBDT’18). ACM, 52--56.
[24]
Mohammad Khoshneshin and W. Nick Street. 2010. Incremental collaborative filtering via evolutionary co-clustering. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). ACM, New York, NY, 325--328.
[25]
Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434.
[26]
Andrea Lancichinetti and Santo Fortunato. 2009. Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 5 (2009), 056117.
[27]
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, and Samy Bengio. 2016. LLORMA: Local low-rank matrix approximation. J. Mach. Learn. Res. 17, 15 (2016), 1--24.
[28]
Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Advances in Neural Information Processing Systems. 2177--2185.
[29]
Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the International Conference on Machine Learning (ICML’09).
[30]
Stan Z. Li, Xin Wen Hou, Hong Jiang Zhang, and Qian Sheng Cheng. 2001. Learning spatially localized, parts-based representation. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’01), Vol. 1. IEEE, I--I.
[31]
Guang Ling, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Online learning for collaborative filtering. In Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN’12). IEEE, 1--8.
[32]
Jing Lu, Steven Hoi, and Jialei Wang. 2013. Second order online collaborative filtering. In Proceedings of the Asian Conference on Machine Learning. 325--340.
[33]
Hao Ma. 2014. On measuring social friend interest similarities in recommender systems. In Proceedings of the Association for Computing Machinery’s Special Interest Group on Information Retrieval Conference (SIGIR’14).
[34]
Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the Conference on Information and Knowledge Management (CIKM’08).
[35]
Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 287--296.
[36]
Alan Mislove, Bimal Viswanath, Krishna P. Gummadi, and Peter Druschel. 2010. You are who you know: Inferring user profiles in online social networks. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 251--260.
[37]
Weike Pan. 2016. A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177 (2016), 447--453.
[38]
Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’10).
[39]
Symeon Papadopoulos, Yiannis Kompatsiaris, Athena Vakali, and Ploutarchos Spyridonos. 2012. Community detection in social media. Data Min. Knowl. Discov. 24, 3 (2012), 515--554.
[40]
Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 3 (2007), 036106.
[41]
Jasson D. M. Rennie and Nathan Srebro. 2005. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning. ACM, 713--719.
[42]
Suman Deb Roy, Tao Mei, Wenjun Zeng, and Shipeng Li. 2013. Towards cross-domain learning for social video popularity prediction. IEEE Transactions on Multimedia (2013).
[43]
Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Nips, Vol. 1. 2--1.
[44]
Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th International Conference on Machine Learning. ACM, 880--887.
[45]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW’01). ACM, New York, NY, 285--295.
[46]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, 285--295.
[47]
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web. ACM, 111--112.
[48]
Hanhuai Shan and Arindam Banerjee. 2008. Bayesian co-clustering. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). IEEE, 530--539.
[49]
Jiliang Tang, Huiji Gao, and Huan Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM’12). ACM, New York, NY, 93--102.
[50]
Jialei Wang, Steven C. H. Hoi, Peilin Zhao, and Zhi-Yong Liu. 2013. Online multi-task collaborative filtering for on-the-fly recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 237--244.
[51]
Yu Wang, Gao Cong, Guojie Song, and Kunqing Xie. 2010. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1039--1048.
[52]
Lin Xiao and Gu Zhaoquan. 2017. Modeling implicit communities in recommender systems. In International Conference on Web Information Systems Engineering. Springer, 387--402.
[53]
Wang Xin, Lu Wei, Martin Ester, Can Wang, and Chun Chen. 2016. Social recommendation with strong and weak ties. In Proceedings of the Conference on Information and Knowledge Management (CIKM’16).
[54]
Jaewon Yang, Julian McAuley, and Jure Leskovec. 2013. Community detection in networks with node attributes. In Proceedings of the IEEE 13th International Conference on Data Mining (ICDM’13). IEEE, 1151--1156.
[55]
Yun Yang and David B. Dunson. 2013. Sequential Markov chain Monte Carlo. arXiv: Statistics Theory (2013).
[56]
Yuan Yao, Wayne Xin Zhao, Yaojing Wang, Hanghang Tong, Feng Xu, and Jian Lu. 2017. Version-aware rating prediction for mobile app recommendation. ACM Trans. Inf. Syst. 35, 4, Article 38 (Jun. 2017), 33 pages.
[57]
Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A spatial item recommender system. ACM Trans. Inf. Syst. 32, 3 (2014), 11--11.
[58]
Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia Sadiq. 2015. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans. Inf. Syst. 35, 2 (2015), 1631--1640.
[59]
Hongzhi Yin, Zhiting Hu, Xiaofang Zhou, Hao Wang, Kai Zheng, Quoc Viet Hung Nguyen, and Shazia Sadiq. 2016. Discovering interpretable geo-social communities for user behavior prediction. In Proceedings of the IEEE International Conference on Data Engineering. 942--953.
[60]
Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans. Knowl. Data Eng. 29, 11 (2017), 2537--2551.
[61]
Hongzhi Yin, Xiaofang Zhou, Bin Cui, Hao Wang, Kai Zheng, and Quoc Viet Hung Nguyen. 2016. Adapting to user interest drift for POI recommendation. IEEE Trans. Knowl. Data Eng. 28, 10 (2016), 2566--2581.
[62]
Sheng Zhang, Weihong Wang, James Ford, and Fillia Makedon. 2006. Learning from incomplete ratings using non-negative matrix factorization. In Proceedings of the SIAM International Conference on Data Mining (SDM’06), Vol. 6. SIAM, 548--552.
[63]
Yongfeng Zhang, Min Zhang, Yiqun Liu, and Shaoping Ma. 2013. Improve collaborative filtering through bordered block diagonal form matrices. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). ACM, New York, NY, 313--322.
[64]
Tong Zhao, Julian McAuley, and Irwin King. 2014. Leveraging social connections to improve personalized ranking for collaborative filtering. In Proceedings of the Conference on Information and Knowledge Management (CIKM’14).
[65]
Zhou Zhao, James Cheng, Furu Wei, Ming Zhou, Wilfred Ng, and Yingjun Wu. 2014. SocialTransfer: Transferring social knowledge for cold-start cowdsourcing. 779--788.
[66]
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A neural autoregressive approach to collaborative filtering. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML’16), Vol. 48. JMLR.org, 764--773.
[67]
Erheng Zhong, Wei Fan, and Qiang Yang. 2014. User behavior learning and transfer in composite social networks. ACM Trans. Knowl. Discov. Data 8, 1, Article 6 (2014), 32 pages.
[68]
Yin Zhu, Yuqiang Chen, Zhongqi Lu, Sinno Jialin Pan, Gui-Rong Xue, Yong Yu, and Qiang Yang. 2011. Heterogeneous transfer learning for image classification. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’11).

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cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 37, Issue 4
October 2019
299 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/3357218
Issue’s Table of Contents
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Publication History

Published: 26 November 2019
Accepted: 01 July 2019
Revised: 01 June 2019
Received: 01 March 2018
Published in TOIS Volume 37, Issue 4

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Author Tags

  1. Bayesian inference
  2. Recommendation
  3. implicit preference community
  4. online learning
  5. transfer learning

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • Natural Science Foundation of China
  • National Key Research and Development Program of China

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