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RECSYS
2010
ACM

List-wise learning to rank with matrix factorization for collaborative filtering

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List-wise learning to rank with matrix factorization for collaborative filtering
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 analytically 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 related state-of-the-art collaborative ranking approach (CoFiRank). Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval
Yue Shi, Martha Larson, Alan Hanjalic
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where RECSYS
Authors Yue Shi, Martha Larson, Alan Hanjalic
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