Symmetrical information filtering via punishing superfluous diffusion

Xuzhen Zhu, Hui Tian and Tianqiao Zhang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 508, issue C, 1-9

Abstract: For niche recommendation, information filtering has attracted much attention from various fileds. Especially, mass diffusion based models behave prominently. Nevertheless, these models intrinsically suffer superfluous diffusion, transferring redundant preferences to the object and damaging the accuracy, diversity, and personalization of recommendation. Besides, we discover that the symmetrical diffusion can effectively improve recommendation performances. Thus, we assume that the superfluous diffusion should be symmetrically punished. Hence, we propose a symmetrical punishment model on superfluous diffusion for accurate information recommendation. Extensive experiments on two data sets Netflix and Movielens show that our proposed model outperforms mainstream indices remarkably in accuracy, diversity and personalization.

Keywords: Information filtering; Personalized recommendation; Symmetrical punishment; Superfluous diffusion (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:508:y:2018:i:c:p:1-9

DOI: 10.1016/j.physa.2018.05.065

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