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Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö >
TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
Current Result Document :
4
/ 4
ÀÌÀü°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System
¿µ¹®Á¦¸ñ(English Title)
MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System
ÀúÀÚ(Author)
Jianli Zhao
Zhengbin Fu
Qiuxia Sun
Sheng Fang
Wenmin Wu
Yang Zhang
Wei Wang
¿ø¹®¼ö·Ïó(Citation)
VOL 13 NO. 05 PP. 2381 ~ 2399 (2019. 05)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(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.
Å°¿öµå(Keyword)
Top-N recommendation
Collaborative Filtering (CF)
learning to rank (LTR)
Mean Average Precision (MAP)
implicit feedback.
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