Journal
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Volume 13, Issue 5, Pages 2381-2399Publisher
KSII-KOR SOC INTERNET INFORMATION
DOI: 10.3837/tiis.2019.05.008
Keywords
Top-N recommendation; Collaborative Filtering (CF); learning to rank (LTR); Mean Average Precision (MAP); implicit feedback
Funding
- National Key RD Plan [2018YFC0831002, 2017YFC0804406]
- Key R&D Plan of Shandong Province [2018GGX101045]
- Humanity and Social Science Fund of the Ministry of Education [18YJAZH136, 17YJCZH262]
- Key Project of Industrial Transformation and Upgrading (Made in China 2025) [TC170A5SW]
- National Natural Science Foundation of China [61433012, U1435215]
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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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