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Collaborative personal profiling for web service ranking and recommendation

Author

Listed:
  • Wenge Rong

    (Beihang University
    Beihang University
    Research Institute of Beihang University in Shenzhen)

  • Baolin Peng

    (Beihang University
    Research Institute of Beihang University in Shenzhen)

  • Yuanxin Ouyang

    (Beihang University
    Beihang University
    Research Institute of Beihang University in Shenzhen)

  • Kecheng Liu

    (University of Reading
    Shanghai University of Finance and Economics)

  • Zhang Xiong

    (Beihang University
    Beihang University
    Research Institute of Beihang University in Shenzhen)

Abstract

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Suggested Citation

  • Wenge Rong & Baolin Peng & Yuanxin Ouyang & Kecheng Liu & Zhang Xiong, 2015. "Collaborative personal profiling for web service ranking and recommendation," Information Systems Frontiers, Springer, vol. 17(6), pages 1265-1282, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-014-9495-4
    DOI: 10.1007/s10796-014-9495-4
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    References listed on IDEAS

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    Cited by:

    1. Mingxin Gan & Lily Sun & Rui Jiang, 2019. "GLORY: Exploration and integration of global and local correlations to improve personalized online social recommendations," Information Systems Frontiers, Springer, vol. 21(4), pages 925-939, August.
    2. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
    3. Raghav Pavan Karumur & Tien T. Nguyen & Joseph A. Konstan, 2018. "Personality, User Preferences and Behavior in Recommender systems," Information Systems Frontiers, Springer, vol. 20(6), pages 1241-1265, December.
    4. Kimberly García & Sonia Mendoza & Dominique Decouchant & Patrick Brézillon, 2018. "Facilitating resource sharing and selection in ubiquitous multi-user environments," Information Systems Frontiers, Springer, vol. 20(5), pages 1075-1095, October.
    5. Sowmya Kamath S & Ananthanarayana V. S., 2019. "Discovering composable web services using functional semantics and service dependencies based on natural language requests," Information Systems Frontiers, Springer, vol. 21(1), pages 175-189, February.
    6. Sowmya Kamath S & Ananthanarayana V. S., 0. "Discovering composable web services using functional semantics and service dependencies based on natural language requests," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    7. Richard K. Lomotey & Ralph Deters, 0. "Middleware for mobile medical data management with minimal latency," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    8. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.
    9. Richard K. Lomotey & Ralph Deters, 2018. "Middleware for mobile medical data management with minimal latency," Information Systems Frontiers, Springer, vol. 20(6), pages 1281-1296, December.
    10. Kimberly García & Sonia Mendoza & Dominique Decouchant & Patrick Brézillon, 0. "Facilitating resource sharing and selection in ubiquitous multi-user environments," Information Systems Frontiers, Springer, vol. 0, pages 1-21.

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