Collaborative filtering aims at predicting a user's interest for a given item based on a collection of user profiles. This article views collaborative filtering as a problem highly related to information retrieval, drawing an analogy between the concepts of users and items in recommender systems and queries and documents in text retrieval. We present a probabilistic user-to-item relevance framework that introduces the concept of relevance into the related problem of collaborative filtering. Three different models are derived, namely, a user-based, an item-based, and a unified relevance model, and we estimate their rating predictions from three sources: the user's own ratings for different items, other users' ratings for the same item, and ratings from different but similar users for other but similar items. To reduce the data sparsity encountered when estimating the probability density function of the relevance variable, we apply the nonparametric (data-driven) density estimation technique known as the Parzen-window method (or kernel-based density estimation). Using a Gaussian window function, the similarity between users and/or items would, however, be based on Euclidean distance. Because the collaborative filtering literature has reported improved prediction accuracy when using cosine similarity, we generalize the Parzen-window method by introducing a projection kernel. Existing user-based and item-based approaches correspond to two simplified instantiations of our framework. User-based and item-based collaborative filterings represent only a partial view of the prediction problem, where the unified relevance model brings these partial views together This work was done when J. Wang was with the Department of Mediamatics, Delft University of Technology. Authors' addresses: J. Wang Adastral Park Campus, University College London, Ross Building, Adastral Park, Martlesham Health, Ipswich Suffolk, IP5 3RE, UK; email: j.wang@adastral. ucl.ac.uk; A. P. de Vries, CWI, PO Box 94079, NL-1090 GB Amsterdam, The Netherlands; email: arjen@acm.org; M. J. T. Reinders, Department of Mediamatics, Delft University of Technology, Elektrotechn., Wisk. and Inform., Room HB 11.300, Mekelweg 4, 2628CD Delft, The Netherlands; email: j.t.reinders@tudelft.nl. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701, USA, fax +1 (212) Wang, J., de Vries, A. P., and Reinders, M. J. T. 2008. Unified relevance models for rating prediction in collaborative filtering.
In that work, together with REF , the authors presented a probabilistic relevance framework, where three models are derived: one based on users, another based on items, and a unified relevance model.
Unified relevance models for rating prediction in collaborative filtering
TOIS
Computer Science