Personalized Collaborative Filtering for Web Services
Author(s):
Tejaswi Middela, K. S. Sadasiva Rao, Dr. I.Satyanarayana
Keywords:
Web services, service recommendation, QoS prediction, collaborative filtering, location-aware
Abstract
Within this paper, we advise an area-aware personalized CF way of Web service recommendation. Although several CF-based internets service QoS conjecture techniques happen to be suggested recently, the performance still needs significant improvement. First of all, existing QoS conjecture techniques rarely consider personalized influence of customers and services when calculating the similarity between customers and between services. Next, Web service QoS factors, for example response some time and throughput, usually is dependent around the locations of Web services and customers. Collaborative Filtering (CF) is broadly useful for making Web service recommendation. CF-based Web service recommendation aims to calculate missing QoS (Quality-of-Service) values of Web services. However, existing Web service QoS conjecture techniques rarely required this observation into account. The experimental results indicate our approach increases the QoS conjecture precision and computational efficiency considerably, in comparison to previous CF-based techniques. The suggested method leverages both locations of customers and Web services when choosing similar neighbors for that target user or service. The technique includes a superior similarity measurement for customers and Web services, by considering the personalized influence of these. To judge the performance in our suggested method, we conduct some comprehensive experiments utilizing a real-world Web service dataset.
Article Details
Unique Paper ID: 143945

Publication Volume & Issue: Volume 3, Issue 4

Page(s): 118 - 122
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