? Hashtag Recommendation Based on Multi-Features of Microblogs
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :711-726    DOI: 10.1007/s11390-018-1851-2
Special Issue on Software Engineering for High-Confidence Systems Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Hashtag Recommendation Based on Multi-Features of Microblogs
Fei-Fei Kou, Jun-Ping Du*, Distinguished Member, CCF, Cong-Xian Yang, Yan-Song Shi, Wan-Qiu Cui Mei-Yu Liang, Yue Geng
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science Beijing University of Posts and Telecommunications, Beijing 100876, China

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Abstract Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features (i.e., user, hashtag, text) of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.
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Keywordshashtag recommendation   topic model   collaborative filtering method   microblog     
Received 2018-01-14;

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61320106006, 61532006, 61772083, and 61502042, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2017RC39.

Corresponding Authors: Jun-Ping Du,E-mail:junpingd@bupt.edu.cn     Email: junpingd@bupt.edu.cn
About author: Fei-Fei Kou currently is a Ph.D. candidate in computer science and technology at Beijing University of Posts and Telecommunications, Beijing. She received her B.S. degree in electronic information engineering from Yantai University, Yantai, in 2010, and M.S. degree in computer technology from Beijing Technology and Business University, Beijing, in 2013. Her major research interest includes semantic learning and multimedia information retrieval and recommendation.
Cite this article:   
Fei-Fei Kou, Jun-Ping Du, Cong-Xian Yang, Yan-Song Shi, Wan-Qiu Cui, Mei-Yu Liang, Yue Geng.Hashtag Recommendation Based on Multi-Features of Microblogs[J]  Journal of Computer Science and Technology, 2018,V33(4): 711-726
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