Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

TL;DR

This paper introduces a novel personalized recommendation algorithm leveraging integrated diffusion on user-item-tag tripartite graphs, which enhances accuracy, diversification, and novelty especially in sparse data scenarios.

Contribution

The paper proposes a new diffusion-based recommendation method that effectively incorporates tag information in tripartite graphs, improving recommendation quality over existing approaches.

Findings

Tag information significantly improves recommendation accuracy.

The method enhances diversification and novelty of recommendations.

Experimental results on benchmark datasets validate the approach.

Abstract

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this paper, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.