Jason Weston
ABSTRACT
Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.
- J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, et al. The youtube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems, pages 293--296. ACM, 2010. Google Scholar
Digital Library
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133--151, 2001. Google ScholarDigital Library
- D. Grangier and S. Bengio. A discriminative kernel-based model to rank images from text queries. PAMI, 30:1371--1384, 2008. Google ScholarDigital Library
- R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. NIPS, pages 115--132, 1999.Google Scholar
- Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems, pages 139--146. ACM, 2012. Google ScholarDigital Library
- N. Usunier, D. Buffoni, and P. Gallinari. Ranking with ordered weighted pairwise classification. ICML, 2009. Google ScholarDigital Library
- M. Weimer, A. Karatzoglou, Q. Le, A. Smola, et al. Cofirank-maximum margin matrix factorization for collaborative ranking. NIPS, 2007.Google Scholar
- J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, pages 2764--2770, 2011. Google ScholarDigital Library
- J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. ICML, 2012.Google Scholar
- F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li. Listwise approach to learning to rank: theory and algorithm. In ICML, 2008. Google ScholarDigital Library
- Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR, pages 271--278, 2007. Google ScholarDigital Library
Index Terms
- Learning to rank recommendations with the k-order statistic loss
Recommendations
List-wise learning to rank with matrix factorization for collaborative filtering
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsA ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is obtained by minimizing a loss function that represents the ...
Learning to rank for hybrid recommendation
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementMost existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the advantages of the two for improved recommendation performance. Traditional recommender ...
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsIn 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 ...
Comments