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CLiMF: collaborative less-is-more filtering

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, Alan Hanjalic

CLiMF: collaborative less-is-more filtering

AAAI Press, Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, August 2013



In 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 binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filter- ing (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known in- formation retrieval metric for capturing the performance of top-k recommendations. We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric. Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods. 

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