• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System


Abstract

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.


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Cite this article

[IEEE Style]
Jianli Zhao, Zhengbin Fu, Qiuxia Sun, Sheng Fang, Wenmin Wu, Yang Zhang and Wei Wang, "MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System," KSII Transactions on Internet and Information Systems, vol. 13, no. 5, pp. 2381-2399, 2019. DOI: 10.3837/tiis.2019.05.008

[ACM Style]
Zhao, J., Fu, Z., Sun, Q., Fang, S., Wu, W., Zhang, Y., and Wang, W. 2019. MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System. KSII Transactions on Internet and Information Systems, 13, 5, (2019), 2381-2399. DOI: 10.3837/tiis.2019.05.008