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Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm

Author

Listed:
  • Chen Wei

    (Lenovo China)

  • Richard Khoury

    (Lakehead University)

  • Simon Fong

    (University of Macau)

Abstract

Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and the Web 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have created an abundance of information about users and their interests. The computational challenge however is to analyze and filter this information in order to generate useful recommendations for each user. Collaborative Filtering (CF) is a recommendation service technique that collects information from a user’s preferences and from trusted peer users in order to infer a new targeted suggestion. CF and its variants have been studied extensively in the literature on online recommending, marketing and advertising systems. However, most of the work done was based on Web 1.0, where all the information necessary for the computations is assumed to always be completely available. By contrast, in the distributed environment of Web 2.0, such as in current social networks, the required information may be either incomplete or scattered over different sources. In this paper, we propose the Multi-Collaborative Filtering Trust Network algorithm, an improved version of the CF algorithm designed to work on the Web 2.0 platform. Our simulation experiments show that the new algorithm yields a clear improvement in prediction accuracy compared to the original CF algorithm.

Suggested Citation

  • Chen Wei & Richard Khoury & Simon Fong, 2013. "Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm," Information Systems Frontiers, Springer, vol. 15(4), pages 533-551, September.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:4:d:10.1007_s10796-012-9377-6
    DOI: 10.1007/s10796-012-9377-6
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    References listed on IDEAS

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    1. Luisa Massari, 2010. "Analysis of MySpace user profiles," Information Systems Frontiers, Springer, vol. 12(4), pages 361-367, September.
    2. Jae-gu Song & Seoksoo Kim, 2009. "A study on applying context-aware technology on hypothetical shopping advertisement," Information Systems Frontiers, Springer, vol. 11(5), pages 561-567, November.
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