Local Latent Space Models for Top-N Recommendation

ABSTRACT

Users' behaviors are driven by their preferences across various aspects of items they are potentially interested in purchasing, viewing, etc. Latent space approaches model these aspects in the form of latent factors. Although such approaches have been shown to lead to good results, the aspects that are important to different users can vary. In many domains, there may be a set of aspects for which all users care about and a set of aspects that are specific to different subsets of users. To explicitly capture this, we consider models in which there are some latent factors that capture the shared aspects and some user subset specific latent factors that capture the set of aspects that the different subsets of users care about.

In particular, we propose two latent space models: rGLSVD and sGLSVD, that combine such a global and user subset specific sets of latent factors. The rGLSVD model assigns the users into different subsets based on their rating patterns and then estimates a global and a set of user subset specific local models whose number of latent dimensions can vary.

The sGLSVD model estimates both global and user subset specific local models by keeping the number of latent dimensions the same among these models but optimizes the grouping of the users in order to achieve the best approximation. Our experiments on various real-world datasets show that the proposed approaches significantly outperform state-of-the-art latent space top-N recommendation approaches.

References

1. {n. d.}. Flixster Dataset. http://http://www.cs.sfu.ca/~sja25/personal/datasets/. ({n. d.}).
2. 2018. Local Latent Space Models for Top-N Recommendation Appendix - Technical Report. https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/18-010. pdf. (2018).
3. James Bennett, Stan Lanning, et al. 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol. 2007. New York, NY, USA, 35.
4. Michael W Berry. 1992. Large-scale sparse singular value computations. The International Journal of Supercomputing Applications 6, 1 (1992), 13--49.
5. Alex Beutel, Ed H Chi, Zhiyuan Cheng, Hubert Pham, and John Anderson. 2017. Beyond Globally Optimal: Focused Learning for Improved Recommendations. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 203--212.
6. Evangelia Christakopoulou and George Karypis. 2016. Local item-item models for top-n recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 67--74.
7. Konstantina Christakopoulou and Arindam Banerjee. 2015. Collaborative Ranking with a Push at the Top. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 205--215.
8. Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 39--46.
9. Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 143--177.
10. Thomas George and Srujana Merugu. 2005. A scalable collaborative filtering framework based on co-clustering. In Data Mining, Fifth IEEE international conference on. IEEE, 4--pp.
11. Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. 2001. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4, 2 (2001), 133--151.
12. Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. LibRec: A Java Library for Recommender Systems.. In UMAP Workshops.
13. F Maxwell Harper and Joseph A Konstan. 2016. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4 (2016), 19.
14. Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 263--272.
15. George Karypis. 2002. CLUTO-a clustering toolkit. Technical Report. MINNESOTA UNIV MINNEAPOLIS DEPT OF COMPUTER SCIENCE.
16. 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.
17. Yehuda Koren. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 1 (2010), 1.
18. Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2014. Local collaborative ranking. In Proceedings of the 23rd international conference on World wide web. ACM, 85--96.
19. Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2013. Local low-rank matrix approximation. In International Conference on Machine Learning. 82--90.
20. Joonseok Lee, Mingxuan Sun, and Guy Lebanon. 2012. Prea: Personalized recommendation algorithms toolkit. Journal of Machine Learning Research 13, Sep (2012), 2699--2703.
21. Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, 497--506.
22. Mark O'Connor and Jon Herlocker. 1999. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR workshop on recommender systems, Vol. 128. UC Berkeley.
23. Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 502--511.
24. 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.
25. Badrul M Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2002. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the fifth international conference on computer and information technology, Vol. 1.
26. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Hanjalic. 2012. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 139--146.
27. Pang-Ning Tan et al. 2007. Introduction to data mining. Pearson Education India.
28. Jason Weston, Ron J Weiss, and Hector Yee. 2013. Nonlinear latent factorization by embedding multiple user interests. In Proceedings of the 7th ACM conference on Recommender systems. ACM, 65--68.
29. Bin Xu, Jiajun Bu, Chun Chen, and Deng Cai. 2012. An exploration of improving collaborative recommender systems via user-item subgroups. In Proceedings of the 21st international conference on World Wide Web. ACM, 21--30.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems

  • Information systems applications

    • Data mining

      • Clustering

      • Collaborative filtering