A belief network model for microblog recommendation

doi:10.3969/j.issn.1000-1565.2022.04.015

Abstract

Abstract: Considering that the existing microblog recommendation methods or models are inconvenient to combine evidence, this paper proposes a new microblog recommendation method. The belief network is applied to microblog recommendation, a basic belief network recommendation model is then constructed, and an extended microblog recommendation model is proposed by integrating the user interaction microblog evidence into the basic model. The nodes in the model represent microblogs, users and interest feature words, while the directed arcs represent relationships between nodes. The relevance between user and microblog is obtained by calculating the degree of coverage between user and microblog. The experimental results on microblog datasets show that the F-measure of the extended model is at least 4.9% higher than that of the basic model. Compared with existing recommendation methods, the new model is more effective in improving recommendation performance in terms of combining evidence.

Key words: microblog recommendation, belief network, combining evidence, interaction microblog

CLC Number:

TP391

ZHAO Kaihua, XU Jianmin, BAO Caiqian. A belief network model for microblog recommendation[J]. Journal of Hebei University(Natural Science Edition), 2022, 42(4): 438-448.

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