Information filtering based on transferring similarity

Abstract

In this Brief Report, we propose a new index of user similarity, namely the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E {\bf 73} 026120 (2006)], and is relevant to the missing link prediction problem.

4 pages, 4 figures

References in corpus (10)

• Hierarchical structure and the prediction of missing links in networks
• How to project a bipartite network?
• Vertex similarity in networks
• Heat Conduction Process on Community Networks as a Recommendation Model
• Effect of initial configuration on network-based recommendation
• Recommendation model based on opinion diffusion
• Personal Recommendation via Modified Collaborative Filtering
• Information Filtering via Self-Consistent Refinement
• Improved Collaborative Filtering Algorithm via Information Transformation
• Information Flow in Interaction Networks

Cited by in corpus (22)

• Link Prediction in Complex Networks: A Survey
• Predicting Missing Links via Local Information
• Recommender Systems
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• Link Prediction Based on Local Random Walk
• Link Prediction with Node Clustering Coefficient
• Information filtering via biased heat conduction
• Collaborative filtering with diffusion-based similarity on tripartite graphs
• Highly accurate recommendation algorithm based on high-order similarities
• Solving the accuracy-diversity dilemma via directed random walks
• Information Filtering via Collaborative User Clustering Modeling
• Effect of user tastes on personalized recommendation
• Degree correlation effect of bipartite network on personalized recommendation
• Collaborative filtering based on multi-channel diffusion
• Improving local clustering based top-L link prediction methods via asymmetrical link clustering information
• Finding missing edges and communities in incomplete networks
• Ultra accurate collaborative information filtering via directed user similarity
• Collaborative Filtering Approach to Link Prediction
• Information Filtering on Coupled Social Networks
• A two-step Recommendation Algorithm via Iterative Local Least Squares
• DSSLP: A Distributed Framework for Semi-supervised Link Prediction
• Impacts of Data Splitting Strategies on Parameterized Link Prediction Algorithms