Web Service Recommendation using Optimized Iterative Collaborative Filtering

DOI

10.2991/csss-14.2014.115How to use a DOI?

Keywords

Iteration, Predicting Tree, Collaborative Filtering, Web Service Recommendation, QoS

Abstract

With the explosive growth of web services on the World Wide Web, service recommendation is becoming extremely important to both the service providers and the active users. In this paper, we propose a web service recommendation model which utilizes the prediction of Quality-of-Services (QoS) based on collaborative filtering with optimized iteration. The benefit of employing iteration in collaborative filtering is that the prediction accuracy of QoS values can be raised significantly via recursive refinement. Since such iteration scheme will hinder training performance, a novel optimization strategy is introduced based on the predicting tree. Finally, the optimized model is implemented and deployed to conduct the experiments on a real-world data set, which contains 1.5 million web services invocation information. The experimental results show that our model has achieved better prediction accuracy than other models with similar performance.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wang Binbin
AU  - Guo Jie
AU  - Zhou Zuojian
AU  - Pan Jingui
PY  - 2014/06
DA  - 2014/06
TI  - Web Service Recommendation using Optimized Iterative Collaborative Filtering
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 490
EP  - 494
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.115
DO  - 10.2991/csss-14.2014.115
ID  - Binbin2014/06
ER  -