Incorporating Bayesian inference with random walk for friend recommendations

doi:10.3969/j.issn.1000-5641.2018.04.008

Abstract

Abstract: Random walk is an effective strategy for dealing with a large user base as well as data sparsity in recommendation problems. However, the current work on recommendation problems do not take full account of the impact implied by both the intimacy difference between users and the reverse social influence. This paper presents an optimized friend recommendation model based on random walk, which introduces frequent pattern mining to capture user intimacy and to optimize the transition probability matrix,and is combined with local reverse search to implement recommendations. In order to make full use of users' attribute information, a Bayesian inference model is proposed for analyzing users' potential friend relationships and combined with random walk to provide better recommendation services. Experiments on real datasets demonstrated the effectiveness of the proposed method.

Key words: friend recommendations, random walk, Bayesian inference

CLC Number:

TP391

YANG Qing, WANG Hai-yang, BIAN Meng-yang, ZHANG Jing-wei, LIN Yu-ming, ZHANG Hui-bing, ZHANG Hai-tao. Incorporating Bayesian inference with random walk for friend recommendations[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(4): 80-89.

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