Collaborative filtering based on multi-channel diffusion

TL;DR

This paper introduces a novel similarity measure for collaborative filtering based on a multi-channel diffusion process on user-object graphs, improving accuracy over traditional methods.

Contribution

It proposes a new multi-channel diffusion-based similarity measure for collaborative filtering, enhancing accuracy in user similarity estimation.

Findings

The diffusion-based similarity outperforms Pearson correlation in accuracy.

Multi-channel representation effectively captures rating information.

The method improves recommendation quality in collaborative filtering.

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.