List-wise Learning to Rank with Matrix Factorization for Collaborative Filtering

TitleList-wise Learning to Rank with Matrix Factorization for Collaborative Filtering
Publication TypeConference Paper
Year of Publication2010
AuthorsShi, Y, Larson, MA, Hanjalic, A
Refereed DesignationRefereed
Conference NameRecSys '10: Proceedings of the fourth ACM conference on Recommender systems
Pagination269-272
PublisherACM
Conference LocationBarcelona, Spain
ISBN Number978-1-60558-906-0
Keywordscollaborative filtering, learning to rank, matrix factorization, recommendation, Recommender systems
Abstract

A 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 uncertainty between training lists and output lists produced by a MF ranking model ListRank-MF enjoys the advantage of low complexity and is ana-lytically shown to be linear with the number of observed ratings for a given user-item matrix. We also experimentally demonstrate the effectiveness of ListRank-MF by comparing its performance with that of item-based collaborative recommendation and a re-lated state-of-the-art collaborative ranking approach (CoFiRank).

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