Social Network Event Recommendation Algorithms Based on User Similarity Random Walk

doi:10.12068/j.issn.1005-3026.2019.11.003

Abstract

Abstract: Aimed at the problem of insufficient coverage and accuracy of event recommendation based on social network， a user similarity-based Si-user Walker algorithm was proposed. The algorithm abstracts online user group data into graphs through event-based social network features and changes the traditional strategy of random walk based on graph-based topology structure on the basis of restart random walk algorithm. According to geographical location， event types were divided and a new calculation method of user similarity was proposed. Then， the user similarity matrix works as transition probability of random walk. Transfer probability not only preserves the transitivity of graph， but also guarantees the authenticity of graph node walk. Compared with other recommendation algorithms， it is shown that the proposed algorithm experiments on real data sets can improve root mean square error， accuracy and coverage.

Key words: event recommendation, social network, user similarity, topological structure, restart random walk

CLC Number:

TP181

MA Tie-min， ZHOU Fu-cai， WANG Shuang. Social Network Event Recommendation Algorithms Based on User Similarity Random Walk[J]. Journal of Northeastern University Natural Science, 2019, 40(11): 1533-1538.

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