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A Service Recommendation Algorithm Based on Modeling of Dynamic and Diverse Demands

Author

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  • Yanmei Zhang

    (Information School, Central University of Finance and Economics, Beijing, China)

  • Tingpei Lei

    (Information School, Central University of Finance and Economics, Beijing, China)

  • Zhiguang Qin

    (University of Electronic Science and Technology of China, Chengdu, China)

Abstract

This article contends that current service recommendation algorithms are still unable to meet the dynamic and diverse demands of users, so a service recommendation algorithm considering dynamic and diverse demands is proposed. The latent Dirichlet allocation model of machine learning field is adopted to extract the user implicit demand factors, and then the bipartite graph modeling and random-walk algorithm are used to extend implicit demand factors to predict short-term changes and diversity of user demand. At last, the service recommendation list is generated based on these demand factors. Experimental results on a real-world data set regarding service composition show that the proposed algorithm can represent dynamic and diverse user demands, and the performance of the proposed algorithm is better than that of the other algorithms in terms of accuracy, novelty, and diversity.

Suggested Citation

  • Yanmei Zhang & Tingpei Lei & Zhiguang Qin, 2018. "A Service Recommendation Algorithm Based on Modeling of Dynamic and Diverse Demands," International Journal of Web Services Research (IJWSR), IGI Global, vol. 15(1), pages 47-70, January.
    • Handle: RePEc:igg:jwsr00:v:15:y:2018:i:1:p:47-70
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