CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering

ABSTRACT

In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring the performance of top-k recommendations. We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric. Experiments on two social network datasets demonstrate the effectiveness and the scalability of CLiMF, and show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.

References

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17(6):734--749, 2005.
2. D. Agarwal and B.-C. Chen. Regression-based latent factor models. KDD '09, pages 19--28. ACM, 2009.
3. S. Balakrishnan and S. Chopra. Collaborative ranking. WSDM '12, pages 143--152. ACM, 2012.
4. C. J. C. Burges, R. Ragno, and Q. V. Le. Learning to Rank with Nonsmooth Cost Functions. In NIPS, pages 193--200. MIT Press, 2006.
5. S. Chakrabarti, R. Khanna, U. Sawant, and C. Bhattacharyya. Structured learning for non-smooth ranking losses. KDD '08, pages 88--96. ACM, 2008.
6. O. Chapelle and M. Wu. Gradient descent optimization of smoothed information retrieval metrics. Inf. Retr., 13:216--235, June 2010.
7. H. Chen and D. R. Karger. Less is more: probabilistic models for retrieving fewer relevant documents. SIGIR '06, pages 429--436. ACM, 2006.
8. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. RecSys '10, pages 39--46. ACM, 2010.
9. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22:143--177, January 2004.
10. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: a free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pages 305--308, New York, NY, USA, 2011. ACM.
11. T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22:89--115, January 2004.
12. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. ICDM '08, pages 263--272, 2008.
13. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30--37, August 2009.
14. Y. Koren and J. Sill. Ordrec: an ordinal model for predicting personalized item rating distributions. RecSys '11, pages 117--124. ACM, 2011.
15. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7:76--80, 2003.
16. N. N. Liu and Q. Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. SIGIR '08, pages 83--90. ACM, 2008.
17. N. N. Liu, M. Zhao, and Q. Yang. Probabilistic latent preference analysis for collaborative filtering. CIKM '09, pages 759--766. ACM, 2009.
18. T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009.
19. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. ICDM '08, pages 502--511, 2008.
20. L. Pizzato, T. Rej, T. Chung, I. Koprinska, and J. Kay. Recon: a reciprocal recommender for online dating. RecSys '10, pages 207--214. ACM, 2010.
21. S. Rendle, C. Freudenthaler, Z. Gantner, and S.-T. Lars. Bpr: Bayesian personalized ranking from implicit feedback. UAI '09, pages 452--461. AUAI Press, 2009.
22. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. CSCW '94, pages 175--186. ACM, 1994.
23. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. volume 20 of NIPS '08, 2008.
24. Y. Shi, A. Karatzoglou, L. Baltrunas, M. A. Larson, A. Hanjalic, and N. Oliver. TFMAP: Optimizing MAP for top-n context-aware recommendation. SIGIR '12. ACM, 2012.
25. Y. Shi, M. Larson, and A. Hanjalic. List-wise learning to rank with matrix factorization for collaborative filtering. RecSys '10, pages 269--272. ACM, 2010.
26. Y. Shi, P. Serdyukov, A. Hanjalic, and M. Larson. Personalized landmark recommendation based on geotags from photo sharing sites. ICWSM '11, pages 622--625. AAAI, 2011.
27. H. Steck. Item popularity and recommendation accuracy. RecSys '11, pages 125--132. ACM, 2011.
28. M. Taylor, J. Guiver, S. Robertson, and T. Minka. Softrank: optimizing non-smooth rank metrics. WSDM '08, pages 77--86. ACM, 2008.
29. E. M. Voorhees. The trec-8 question answering track report. In TREC-8, 1999.
30. J. Wang and J. Zhu. On statistical analysis and optimization of information retrieval effectiveness metrics. SIGIR '10, pages 226--233. ACM, 2010.
31. M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. Cofirank - maximum margin matrix factorization for collaborative ranking. NIPS '07, pages 1593--1600, 2007.
32. J. Xu, T.-Y. Liu, M. Lu, H. Li, and W.-Y. Ma. Directly optimizing evaluation measures in learning to rank. SIGIR '08, pages 107--114. ACM, 2008.
33. S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. SIGIR '11, pages 295--304. ACM, 2011.
34. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. SIGIR '07, pages 271--278. ACM, 2007.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction