Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs

2009/04/13 by Zi‐Ke Zhang, Zi-Ke Zhang, Tao Zhou +2 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Caching and Content Delivery #Recommender Systems and Techniques #cs.IR

paper · pdf · doi:10.1016/j.physa.2009.08.036

published as Physica A 389 (2010) 179-186 · 12 pages, 6 figures, 2 tables

arxiv created 2009/04/13 · openalex publication_date 2009/09/13 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Evaluating collaborative filtering recommender systems
• Effect of initial configuration on network-based recommendation
• Heat Conduction Process on Community Networks as a Recommendation Model
• Probabilistic Latent Semantic Indexing
• The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Cited by

• Tag-Aware Recommender Systems: A State-of-the-Art Survey