期刊
INFORMATION SCIENCES
卷 348, 期 -, 页码 290-304出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.02.005
关键词
Preference Model; Collaborative filtering; Top-N Recomendation; Recommender Systems; Accuracy
资金
- Hankuk University of Foreign Studies Research Fund
- NSF [CNS-1422215]
- Samsung [GRO-175998]
- National Research Foundation of Korea (NRF) grant - Korean Government (MSIP) [NRF-2014R1A2A1A10054151]
- National Research Foundation of Korea (NRF) grant - Ministry of Science, ICT and Future Planning (MSIP), Korea [IITP-2015-H8501-15-1013]
In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well suited for representing qualitative user preferences. In particular, it can be easily implemented with less than 100 lines of codes on top of existing CF algorithms such as user based, item-based, and matrix-factorization-based algorithms. When our preference model is combined to three kinds of CF algorithms, experimental results demonstrate that the preference model can improve the accuracy of all existing CF algorithms such as ATOP and NDCG@25 by 3-24% and 6-98%, respectively. (C) 2016 Elsevier Inc. All rights reserved.
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