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Clustering based interest prediction in social networks

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Abstract

Efficient interest prediction for social networks is critical for both users and service providers for behavior analysis and a series of extension services. However, most existing approaches are inefficient, incomplete or isolated. In this paper, we propose combination of Gaussian and Markov approaches (namely, GAM) as typical soft computing technology for interest prediction of social intelligent multimedia systems. GAM model considers “the number of posted messages” as the only parameter, and defines selection logic to implement either Gaussian or Markov based approaches. Our proposed solution takes the advantage of Gaussian model in prediction accuracy and computation complexity, and advantage of Markov model in high availability. Further experiments illustrate that our solution achieves higher prediction accuracy of 94.3% (without considering the influence of swing users), with the best result achieved ever.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Xianghan Zheng, Wenfei Zheng, Yang Yang & Wenzhong Guo

  2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, China

    Xianghan Zheng, Wenfei Zheng, Yang Yang & Wenzhong Guo

  3. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Victor Chang

Authors
  1. Xianghan Zheng
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  2. Wenfei Zheng
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  3. Yang Yang
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  4. Wenzhong Guo
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  5. Victor Chang
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Correspondence to Xianghan Zheng.

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Zheng, X., Zheng, W., Yang, Y. et al. Clustering based interest prediction in social networks. Multimed Tools Appl 78, 32755–32774 (2019). https://doi.org/10.1007/s11042-018-7009-y

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  • DOI: https://doi.org/10.1007/s11042-018-7009-y

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