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A Novel Hybrid Friends Recommendation Framework for Twitter

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Book cover Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10367))

Abstract

As one of the key features of social networks, friends recommendation is a kind of link prediction task with ranking that was extensively investigated recently in the area of social networks analysis as users would like to follow people who have similar interests to them. We use Twitter as a case study and propose a novel hybrid friends recommendation framework that is not only based on friends relationship but also users’ location information, which are recorded by Twitter when they posted their tweets. Our framework can recommend friends to users who have similar interests based on location features by using collaborative filtering to effectively filter out those common places which are meaningless, e.g., bus station; and focuses on those places that have high probability that people are there more likely to become friends, e.g., dance studio. In addition, we propose a multiple classifiers combination method to leverage the information contained in friends and locations features in order to get better outcomes. We evaluate our framework on two real corpora from Twitter, and the favorable results indicate that our proposed approach is feasible.

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Notes

  1. 1.

    https://dev.twitter.com/docs/streaming-apis.

  2. 2.

    http://nlp.stanford.edu/software/CRF-NER.shtml.

  3. 3.

    http://www.aroundmeapp.com/.

  4. 4.

    http://weibo.com.

  5. 5.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgments

This work was supported by Natural Science Foundation of Guangdong Province, China (No. 2015A030310509), and the S&T Projects of Guangdong Province (No. 2016A030303055, No. 2016B030305004, 2016B010109008), Natural Science Foundation of China (No. 61532018 and No. 61502324).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yan Zhao, Mengdi Jia, Wenyan Yang & Kai Zheng

  2. School of Computer Science, South China Normal University, Guangzhou, China

    Jia Zhu

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  1. Yan Zhao
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  2. Jia Zhu
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  3. Mengdi Jia
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  4. Wenyan Yang
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  5. Kai Zheng
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Correspondence to Jia Zhu .

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Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University , Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Zhao, Y., Zhu, J., Jia, M., Yang, W., Zheng, K. (2017). A Novel Hybrid Friends Recommendation Framework for Twitter. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-63564-4_7

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  • Print ISBN: 978-3-319-63563-7

  • Online ISBN: 978-3-319-63564-4

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