Integrating Community Interest and Neighbor Semantic for Microblog Recommendation

Mingxin Gan and Xiongtao Zhang
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Mingxin Gan: School of Economics and Management, University of Science and Technology, Beijing, China
Xiongtao Zhang: School of Economics and Management, University of Science and Technology, Beijing, China

International Journal of Web Services Research (IJWSR), 2021, vol. 18, issue 2, 54-75

Abstract: As a typical characteristic of microblog information, short text length makes a microblog recommendation hard for new users. Moreover, user cold start makes it difficult to explore accurately the interests of microblog users. Therefore, the authors proposed a microblog recommendation model that integrates both of the users' interest from their communities and the semantic from their neighbors' microblogs. Based on the Kullback-Leibler (KL) language model, the proposed model estimated an interest-based language model and a microblog-based language model. Specifically, the interest-based language model was estimated based on both of the user's word set of interest and that of their community interest. Meanwhile, the microblog-based language model was estimated by combining the word set of a microblog, the neighbor semantic, and the microblog set. Real data from Sina Weibo was crawled to evaluate recommendation performance. Results showed that the proposed model outperforms state-of-art models significantly.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:18:y:2021:i:2:p:54-75

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