Collaborative filtering based on multi-channel diffusion

Ming-Sheng Shang, Ci-Hang Jin, Tao Zhou and Yi-Cheng Zhang

Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, issue 23, 4867-4871

Abstract: In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain rating and a user having voted an object will be connected to the channel corresponding to the rating. Diffusion process taking place on such a user–channel bipartite graph gives a new similarity measure of user pairs, which is further demonstrated to be more accurate than the classical Pearson correlation coefficient under the standard collaborative filtering framework.

Keywords: Recommender systems; Collaborative filtering; Diffusion-based similarity; Complex networks; Infophysics (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:388:y:2009:i:23:p:4867-4871

DOI: 10.1016/j.physa.2009.08.011

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