A vertex similarity index for better personalized recommendation

Ling-Jiao Chen, Zi-Ke Zhang, Jin-Hu Liu, Jian Gao and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2017, vol. 466, issue C, 607-615

Abstract: 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.

Keywords: Vertex similarity; Recommender systems; Personalized recommendations; Information filtering (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:466:y:2017:i:c:p:607-615

DOI: 10.1016/j.physa.2016.09.057

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