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arXiv · 1510.02348

A vertex similarity index for better personalized recommendation

Ling-Jiao Chen·Zi-Ke Zhang·Jin-Hu Liu·Jian Gao·Tao Zhou
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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.

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Ling-Jiao Chen, Zi-Ke Zhang, Jin-Hu Liu, Jian Gao, Tao Zhou. 2016-10-06. A vertex similarity index for better personalized recommendation. https://doi.org/10.1016/j.physa.2016.09.057

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