Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering

With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, call...

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Bibliographic Details
Published in:IEEE transactions on services computing 2013-10, Vol.6 (4), p.573-579
Main Authors: Huifeng Sun, Zibin Zheng, Junliang Chen, Lyu, Michael R.
Format: Article
Language:English
Subjects:
Accuracy
Collaboration
collaborative filtering
Equations
QoS
Quality of service
recommender system
Service recommendation
Sparse matrices
Vectors
Web services
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Summary:With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, called normal recovery collaborative filtering, for personalized web service recommendation. To evaluate the web service recommendation performance of our approach, we conduct large-scale real-world experiments, involving 5,825 real-world web services in 73 countries and 339 service users in 30 countries. To the best of our knowledge, our experiment is the largest scale experiment in the field of service computing, improving over the previous record by a factor of 100. The experimental results show that our approach achieves better accuracy than other competing approaches.
ISSN:1939-1374
1939-1374
2372-0204
DOI:10.1109/TSC.2012.31