TFMAP: Optimizing MAP for top-n context-aware recommendation

TitleTFMAP: Optimizing MAP for top-n context-aware recommendation
Publication TypeConference Paper
Year of Publication2012
AuthorsShi, Y, Karatzoglou, A, Baltrunas, L, Larson, MA, Hanjalic, A, Oliver, N
Refereed DesignationRefereed
Conference NameProceedings of the 35th international ACM SIGIR conference on Research and development in Information Retrieval (Full paper)
Pagination155-164
PublisherACM
Conference LocationPortland, Oregon, USA
Keywordscollaborative filtering, context-aware recommendation, implicit feedback, mean average precision, tensor factorization
Abstract

In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work on context-aware recommendation has mainly focused on explicit feedback data, i.e., ratings. We propose TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information.

The optimization of MAP in a large data collection is computationally too complex to be tractable in practice. To address this computational bottleneck, we present a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency of TFMAP, and to ensure its scalability. We experimentally verify the effectiveness of the proposed fast learning algorithm, and demonstrate that TFMAP significantly outperforms state-of-the-art recommendation approaches.

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