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Integrating implicit feedbacks for time-aware web service recommendations

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Abstract

An increasing number of Web services have been published on the Internet over the past decade due to the rapid development and adoption of the SOA (Services Oriented Architecture) standard. However, in the current state of the Web, recommending suitable Web services to users becomes a challenge due to the huge divergence in published content. Existing Web services recommendation approaches based on collaborative filtering are mainly aiming to QoS (Quality of Service) prediction. Recommending services based on users’ ratings on services are seldomly reported due to the difficulty of collecting such explicit feedback. In this paper, we report a data set of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. Temporal information is becoming an increasingly important factor in service recommendation since time effects may influence users’ preferences on services to a large extent. Based on the collected data set, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.

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Notes

  1. http://www.programmableweb.com/api/apple-swift

  2. https://developer.apple.com/library/prerelease/ios/documentation/Swift/Conceptual/Swift_Programming_Language/ RevisionHistory.html#//apple_ref/doc/uid/TP40014097-CH40-ID459

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Acknowledgments

The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, the National Natural Science Foundation of China under grant No. 61202031, 61373037, the central grant funded Cloud Computing demonstration project of China undertaken by Kingdee Software, the State Key Laboratory of Software Engineering Foundation under the grant No.SKLSE 2014-10-07.

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Authors and Affiliations

  1. College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Gang Tian, Chengai Sun & Yuan Tian

  2. State Key Lab of Software Engineering, Wuhan University, Wuhan, China

    Gang Tian, Jian Wang & Keqing He

Authors
  1. Gang Tian
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  2. Jian Wang
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  3. Keqing He
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  4. Chengai Sun
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  5. Yuan Tian
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Correspondence to Jian Wang.

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Tian, G., Wang, J., He, K. et al. Integrating implicit feedbacks for time-aware web service recommendations. Inf Syst Front 19, 75–89 (2017). https://doi.org/10.1007/s10796-015-9590-1

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