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TempoRec: Temporal-Topic Based Recommender for Social Network Services

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Abstract

With the popularization of social networks, increasing numbers of users choose to use Weibo to get information. However, as the number of users grows, the information on Weibo is also multiplying, making it increasingly difficult for users to find the right information they are interested in. Therefore, how to recommend high-quality friends to follow the Weibo is one of the focuses of studies in Weibo-based personalized services. Based on existing Weibo social networking topologies and content-based hybrid recommendation algorithms, the study proposed a hybrid recommendation algorithm based on social relations and time-sequenced topics, which has been verified using Real Sina Weibo datasets. The results show that the improved hybrid recommendation algorithm works well and achieves better mean average precision (MAP) than existing other counterparts.

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Notes

  1. http://www.useit.com.cn/thread-10921-1-1.html.

  2. http://ictclas.nlpir.org/NLPIR.

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Acknowledgements

This work is supported by China National Natural Science Foundation under Grant 61602519.

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Authors and Affiliations

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Yin Zhang, Zhixiao Tu & Qian Wang

Authors
  1. Yin Zhang
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  2. Zhixiao Tu
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  3. Qian Wang
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Correspondence to Yin Zhang.

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Zhang, Y., Tu, Z. & Wang, Q. TempoRec: Temporal-Topic Based Recommender for Social Network Services. Mobile Netw Appl 22, 1182–1191 (2017). https://doi.org/10.1007/s11036-017-0864-3

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