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Personalized Context-Aware QoS Prediction for Web Services Based on Collaborative Filtering

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 6441))

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Abstract

The emergence of abundant Web Services has enforced rapid evolvement of the Service Oriented Architecture (SOA). To help user selecting and recommending the services appropriate to their needs, both functional and nonfunctional quality of service (QoS) attributes should be taken into account. Before selecting, user should predict the quality of Web Services. A Collaborative Filtering (CF)-based recommendation system is introduced to attack this problem. However, existing CF approaches generally do not consider context, which is an important factor in both recommender system and QoS prediction. Motivated by this, the paper proposes a personalized context-aware QoS prediction method for Web Services recommendations based on the SLOPE ONE approach. Experimental results demonstrate that the suggested approach provides better QoS prediction.

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Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Qi Xie, Kaigui Wu, Jie Xu, Pan He & Min Chen

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. College of Computer Science, Chongqing University, 400030, Chongqing, China

    Jiang Zhong  & Yong Feng  & 

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Xie, Q., Wu, K., Xu, J., He, P., Chen, M. (2010). Personalized Context-Aware QoS Prediction for Web Services Based on Collaborative Filtering. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_36

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Chapter
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eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
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JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
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Purchases are for personal use only


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