Multi-Domain Collaborative Filtering

TL;DR

This paper introduces a multi-domain collaborative filtering framework that leverages relationships between domains to address data sparsity, using probabilistic matrix factorization and adaptive knowledge transfer.

Contribution

It proposes a novel probabilistic framework for multi-domain collaborative filtering that automatically learns domain correlations and corrects biases, improving recommendation accuracy.

Findings

Effective in real-world applications

Outperforms some existing methods

Reduces data sparsity issues

Abstract

Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.