Collaborative filtering based on multi-channel diffusion

Details Collaborative filtering based on multi-channel diffusion Collaborative filtering based on multi-channel diffusion

2009-06-05

arxiv.org/abs/0906.1148v1

Physica A 388 (2009) 4867-4871

Computer Science

Information Retrieval

9 pages, 3 figures

Scientific paper

10.1016/j.physa.2009.08.011

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.

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