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http://dx.doi.org/10.3837/tiis.2019.05.008

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

Zhao, Jianli (College of Computer Science & Engineering, Shandong University of Science and Technology)
Fu, Zhengbin (College of Computer Science & Engineering, Shandong University of Science and Technology)
Sun, Qiuxia (College of Mathematics and Systems Science, Shandong University of Science and Technology)
Fang, Sheng (College of Computer Science & Engineering, Shandong University of Science and Technology)
Wu, Wenmin (College of Computer Science & Engineering, Shandong University of Science and Technology)
Zhang, Yang (College of Computer Science & Engineering, Shandong University of Science and Technology)
Wang, Wei (College of Computer Science & Engineering, Shandong University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.5, 2019 , pp. 2381-2399 More about this Journal
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.
Keywords
Top-N recommendation; Collaborative Filtering (CF); learning to rank (LTR); Mean Average Precision (MAP); implicit feedback;
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