›› 2018, Vol. 33 ›› Issue (4): 711-726.doi: 10.1007/s11390-018-1851-2

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Recommender Systems with Big Data • 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   

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2018-01-14 Revised:2018-05-11 Online:2018-07-05 Published:2018-07-05
  • Contact: Jun-Ping Du,E-mail:junpingd@bupt.edu.cn E-mail: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.
  • Supported by:

    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.

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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