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Context-Aware Web Services Recommendation Based on User Preference Expansion

  • Yakun Hu
  • Xiaoliang FanEmail author
  • Ruisheng Zhang
  • Wenbo Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)

Abstract

Context-Aware Recommender System is known to not only recommend items or services similar to those already rated with the highest score, but also consider the current contexts for personalized Web services recommendation. Specifically, a key step for CARS methods refers to previous service invocation experiences under the similar context of the user to make Quality of Services prediction. Existing works either considered the influence of regional correlations on user preference, or combined the location-aware context with the matrix factorization method. However, the user preference expansion triggered by instant update of user location is not fully observed. For instance, when making Web service recommendation for a user, it is expected to be aware of rapid change of the user location immediately and the expansion of user preference as well. In this paper, we propose a Web services recommendation approach dubbed as CASR-UPE (Context-aware Web Services Recommendation based on User Preference Expansion). First, we model the influence of user location update on user preference. Second, we perform the context-aware similarity mining for updated location. Third, we predict the Quality of Services by Bayesian inference, and thus recommend the best Web service for the user subsequently. Finally, we evaluate the CASR-UPE method on WS-Dream dataset by evaluation matrices such as RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.

Keywords

Context awareness Web service Recommender system QoS Preference expansion 

Notes

Acknowledgment

This work is supported by the grants from Natural Science Foundation of China (No. 61300232); Ministry of Education of China “Chunhui Plan” Cooperation and Research Project (No. Z2012114, Z2014141); Funds of State Key Laboratory for Novel Software Technology, Nanjing University (KFKT2014B09); Fundamental Research Funds for the Central Universities (lzujbky-2015-100); and China Telecom Corp. Gansu Branch Cuiying Funds (lzudxcy-2014-6).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yakun Hu
    • 1
  • Xiaoliang Fan
    • 1
    • 2
    • 3
    Email author
  • Ruisheng Zhang
    • 1
  • Wenbo Chen
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and EngineeringXiamen UniversityXiamenChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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