CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering (Best Paper Award)

TitleCLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering (Best Paper Award)
Publication TypeConference Paper
Year of Publication2012
AuthorsShi, Y, Karatzoglou, A, Baltrunas, L, Larson, MA, Oliver, N, Hanjalic, A
Refereed DesignationRefereed
Conference NameProceedings of the sixth ACM conference on Recommender systems (Full paper)
Pagination139-146
PublisherACM
Conference LocationDublin, Ireland
Keywordscollaborative filtering, learning to rank, less is more, matrix factorization, mean reciprocal rank
Abstract

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 Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring 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 demonstrate the effectiveness and the scalability of CLiMF, and show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.

DOI10.1145/2365952.2365981
Custom 1

Best Paper Award

AttachmentSize
RecSys2012-CLiMF-shi.pdf420.94 KB