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Diffusion-like recommendation with enhanced similarity of objects

Ya-Hui An, Qiang Dong, Chong-Jing Sun, Da-Cheng Nie and Yan Fu

Physica A: Statistical Mechanics and its Applications, 2016, vol. 461, issue C, 708-715

Abstract: In the last decade, diversity and accuracy have been regarded as two important measures in evaluating a recommendation model. However, a clear concern is that a model focusing excessively on one measure will put the other one at risk, thus it is not easy to greatly improve diversity and accuracy simultaneously. In this paper, we propose to enhance the Resource-Allocation (RA) similarity in resource transfer equations of diffusion-like models, by giving a tunable exponent to the RA similarity, and traversing the value of this exponent to achieve the optimal recommendation results. In this way, we can increase the recommendation scores (allocated resource) of many unpopular objects. Experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic show that the modified models can yield remarkable performance improvement compared with the original ones.

Keywords: Recommender systems; Bipartite networks; Resource-allocation similarity; Diffusion-like algorithms (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:461:y:2016:i:c:p:708-715

DOI: 10.1016/j.physa.2016.06.027

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