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Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering
Oct.-Dec. 2013 (vol. 6 no. 4)
pp. 573-579
Zibin Zheng, The Chinese University of Hong Kong, Hong Kong
Junliang Chen, Beijing University of Posts and Telecommunications, Beijing
Michael R. Lyu, The Chinese University of Hong Kong, Hong Kong
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.
Index Terms:
Web services,Quality of service,Collaboration,Vectors,Accuracy,Sparse matrices,Equations,QoS,Service recommendation,collaborative filtering,recommender system
Zibin Zheng, Junliang Chen, Michael R. Lyu, "Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering," IEEE Transactions on Services Computing, vol. 6, no. 4, pp. 573-579, Oct.-Dec. 2013, doi:10.1109/TSC.2012.31
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