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Discovering internal social relationship for influence-aware service recommendation

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Abstract

Existing approaches, such as semantic content-based or Collaborative Filtering-based recommendations, fail to exploit social aspects of services because services lack social relationships and do not consider social influence. In this paper, we propose a methodology for connecting distributed services in a global social service network (GSSN) to facilitate discovering internal social relationship for social influence-aware service recommendation. First, we propose a novel platform for constructing a GSSN by linking distributed services with social links based on quality of social link. We then propose a flexible model of the effective awareness of social influence, which provides a quantitative measure of the strength of influence between services. Next, a novel social influence-aware service recommendation approach is proposed based on GSSN using internal social relationship among services. The experimental results demonstrated that our new approach can solve the service recommendation problem with a low usage threshold and high accuracy, where the user preferences are exploited by a recommend-as-you-go method.

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Notes

  1. See http://projects.semwebcentral.org/projects/owls-tc/

  2. See http://projects.semwebcentral.org/projects/sawsdl-tc/

  3. See http://webservices.seekda.com/, in 2012

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Aizu, Fukushima, Japan

    Wuhui Chen, Incheon Paik & Neil Y. Yen

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  1. Wuhui Chen
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  2. Incheon Paik
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  3. Neil Y. Yen
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Correspondence to Wuhui Chen.

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Chen, W., Paik, I. & Yen, N.Y. Discovering internal social relationship for influence-aware service recommendation. Multimed Tools Appl 76, 18193–18220 (2017). https://doi.org/10.1007/s11042-016-3437-8

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  • DOI: https://doi.org/10.1007/s11042-016-3437-8

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