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Issue No. 06 - June (2017 vol. 29)
ISSN: 1041-4347
pp: 1296-1309
Jingwei Xu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Yuan Yao , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Hanghang Tong , School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Phoenix, AZ
Xianping Tao , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Jian Lu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
ABSTRACT
In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RaPare ) to learn the latent profiles of cold-start users/items. The centerpiece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RaPare strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.
INDEX TERMS
Recommender systems, Collaboration, Interviews, Optimization, Algorithm design and analysis, Calibration, Electronic mail,Recommender systems, cold-start problem, rating comparison strategy
CITATION
Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu, "RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem", IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. , pp. 1296-1309, June 2017, doi:10.1109/TKDE.2016.2615039
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