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

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.

12 pages, 6 figures, 2 tables

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Cited by in corpus (28)

• Link Prediction in Complex Networks: A Survey
• Recommender Systems
• Solving the apparent diversity-accuracy dilemma of recommender systems
• Ranking in evolving complex networks
• Tag-Aware Recommender Systems: A State-of-the-art Survey
• Information filtering via biased heat conduction
• Empirical analysis of web-based user-object bipartite networks
• Information filtering via preferential diffusion
• Hypergraph model of social tagging networks
• A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
• Collaborative filtering with diffusion-based similarity on tripartite graphs
• Recommender Systems for Online and Mobile Social Networks: A survey
• Graph-based Collaborative Ranking
• Information Filtering via Collaborative User Clustering Modeling
• Information filtering via hybridization of similarity preferential diffusion processes
• Recommending investors for new startups by integrating network diffusion and investors' domain preference
• An Evolving model of online bipartite networks
• SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
• Multi-View Collaborative Network Embedding
• Diffusion-like recommendation with enhanced similarity of objects
• Information Filtering on Coupled Social Networks
• Self-organization in social tagging systems
• A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
• Gravity Effects on Information Filtering and Network Evolving
• PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks
• Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs
• A two-step Recommendation Algorithm via Iterative Local Least Squares
• Effective Personalized Recommendation in Collaborative Tagging Systems