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The Recommendation System of Micro-Blog Topic Based on User Clustering

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Abstract

As a type of crowdsensing media, micro-blog has become an important crowdsensing place for a lot of real-time information dissemination and discussion. With the increasing of micro-blog users, there are more and more new topics emerging on this kind of platform, which has made the users difficult in finding out their own interesting topics. To solve this problem, this paper proposes a micro-blog topic recommendation system which can give corresponding suggestions/strategies for users. Firstly, the user relationship (i.e., a user adds a follow hyperlink to another user) in micro-blog can be effectively analyzed and saved to the user graph. In addition, an algorithm of computing user authority (which is similar to the idea of PageRank) is proposed to catch influential users based on the built user graph. Secondly, Topic Feature Graph (TFG) and User Micro-blog Feature Graph (UMFG) are respectively constructed based on the micro-blog text corpus of a topic and the micro-blog texts followed by a given user. Based on TFG and UMFG, User Topic Feature Vector (UTFV) and User Topic Feature Matrix (UTFM) can be achieved. After that, users’ similarity is calculated based on the User Topic Feature Vector and User Topic Feature Matrix to realize the users clustering by the help of the hierarchical clustering algorithm. Incorporating topic heat degree and user authority, the recommendation algorithm is presented to realize Micro-blog topic personalized recommendation within user clustering set. Experiments show that our proposed recommendation system has a good accuracy which is up to 50.2%.

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Acknowledgement

This paper is the extended version of the conference paper of MOBIMEDIA 2016.

This work was supported by the Natural Science Foundation of Anhui Province Universities (No. KJ2015A111), in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.

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Authors and Affiliations

  1. Anhui University of Science & Technology, Huainan, Anhui, 232001, China

    Shunxiang Zhang, Shiyao Zhang & Guangli Zhu

  2. University of Aizu, Aizuwakamatsu, Fukushima, 9658580, Japan

    Neil Y. Yen

Authors
  1. Shunxiang Zhang
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  2. Shiyao Zhang
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  3. Neil Y. Yen
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  4. Guangli Zhu
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Correspondence to Shunxiang Zhang.

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Zhang, S., Zhang, S., Yen, N.Y. et al. The Recommendation System of Micro-Blog Topic Based on User Clustering. Mobile Netw Appl 22, 228–239 (2017). https://doi.org/10.1007/s11036-016-0790-9

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