期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 466, 期 -, 页码 607-615出版社
ELSEVIER
DOI: 10.1016/j.physa.2016.09.057
关键词
Vertex similarity; Recommender systems; Personalized recommendations; Information filtering
资金
- National Natural Science Foundation of China [11222543, 61433014]
- Program for New Century Excellent Talents in University [NCET-11-0070]
- Special Project of Sichuan Youth Science and Technology Innovation Research Team [2013TD0006]
Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index. (C) 2016 Elsevier B.V. All rights reserved.
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