Jason Weston
ABSTRACT
Classical matrix factorization approaches to collaborative filtering learn a latent vector for each user and each item, and recommendations are scored via the similarity between two such vectors, which are of the same dimension. In this work, we are motivated by the intuition that a user is a much more complicated entity than any single item, and cannot be well described by the same representation. Hence, the variety of a user's interests could be better captured by a more complex representation. We propose to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes. The overall recommendation model is then nonlinear where the matching score between a user and a given item is the maximum matching score over each of the user's latent interests with respect to the item's latent representation. We describe a simple, general and efficient algorithm for learning such a model, and apply it to large scale, real-world datasets from YouTube and Google Music, where our approach outperforms existing techniques.
- L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS'09), 2009.Google Scholar
- Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 145--186. Springer, 2011.Google ScholarCross Ref
- N. D. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 601--608. ACM, 2009. Google ScholarDigital Library
- Y. LeCun and Y. Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361, 1995. Google ScholarDigital Library
- R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning, pages 791--798. ACM, 2007. Google ScholarDigital Library
- 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 Intl. Joint Conf. Artificial Intelligence, (IJCAI), pages 2764--2770, 2011. Google ScholarDigital Library
- J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. ICML, 2012.Google Scholar
Index Terms
- Nonlinear latent factorization by embedding multiple user interests
Recommendations
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementIn real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user ...
A preprocessing matrix factorization on collaborative filtering based library book recommendation system
DSIT '18: Proceedings of the 2018 International Conference on Data Science and Information TechnologyNowadays, recommendation systems are widely used to recommend items to the users that are specific to their individual preferences and most appropriate. For this reason, many academic libraries try to establish an effectiveness and efficiency book ...
Collaborative factorization for recommender systems
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalRecommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) ...
Comments