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Temporal-Aware QoS-Based Service Recommendation using Tensor Decomposition

Author

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  • Zhi Li

    (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)

  • Jian Cao

    (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)

  • Qi Gu

    (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)

Abstract

The number of services on the Internet is growing rapidly. Thus, the problem of selecting proper services for most users becomes serious and service recommendation is widely needed. Besides functions, QoS information is also an important factor to be considered when making recommendations to users. However, QoS changes with time. To address and solve these challenges, this paper proposes a temporal-aware QoS-based service recommendation framework, and also comes up with a prediction algorithm based on Tucker decomposition. Moreover, the authors use real-world datasets to verify our method with results better than traditional methods.

Suggested Citation

  • Zhi Li & Jian Cao & Qi Gu, 2015. "Temporal-Aware QoS-Based Service Recommendation using Tensor Decomposition," International Journal of Web Services Research (IJWSR), IGI Global, vol. 12(1), pages 62-74, January.
    • Handle: RePEc:igg:jwsr00:v:12:y:2015:i:1:p:62-74
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