Web Service Recommendation using Optimized Iterative Collaborative Filtering

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.

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