| 한글제목(Korean Title) |
MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System |
| 영문제목(English Title) |
MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System |
| 저자(Author) |
Jianli Zhao
Zhengbin Fu
Qiuxia Sun
Sheng Fang
Wenmin Wu
Yang Zhang
Wei Wang
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| 원문수록처(Citation) |
VOL 13 NO. 05 PP. 2381 ~ 2399 (2019. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Top-N recommendation
Collaborative Filtering (CF)
learning to rank (LTR)
Mean Average Precision (MAP)
implicit feedback.
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| 파일첨부 |
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