Information filtering via preferential diffusion

Abstract

Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.

12 pages, 10 figures, 2 tables

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• Recommender Systems for Online and Mobile Social Networks: A survey
• Behavior patterns of online users and the effect on information filtering
• Solving the accuracy-diversity dilemma via directed random walks
• Information Filtering via Collaborative User Clustering Modeling
• A vertex similarity index for better personalized recommendation
• Information filtering via hybridization of similarity preferential diffusion processes
• Promoting cold-start items in recommender systems
• Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics
• A trust-based recommendation method using network diffusion processes
• Improving information filtering via network manipulation
• A Personalized Recommender System for Pervasive Social Networks
• Information filtering in sparse online systems: recommendation via semi-local diffusion
• Diffusion-like recommendation with enhanced similarity of objects
• Information Filtering on Coupled Social Networks
• Spectral coarse graining for random walk in bipartite networks
• Gravity Effects on Information Filtering and Network Evolving
• Membership in social networks and the application in information filtering
• Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts
• The Atlas for the Aspiring Network Scientist