Recommendation model based on opinion diffusion

Abstract

Information overload in the modern society calls for highly efficient recommendation algorithms. In this letter we present a novel diffusion based recommendation model, with users' ratings built into a transition matrix. To speed up computation we introduce a Green function method. The numerical tests on a benchmark database show that our prediction is superior to the standard recommendation methods.

5 pages, 2 figures

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• Degree correlation effect of bipartite network on personalized recommendation
• Personalized recommendation with corrected similarity
• Collaborative filtering based on multi-channel diffusion
• A Personalized Recommender System for Pervasive Social Networks
• Information filtering in sparse online systems: recommendation via semi-local diffusion
• Gravity Effects on Information Filtering and Network Evolving
• Determinable and interpretable network representation for link prediction