A Granular SVM-Based Method for Top-N Web Services Recommendation

Lifang Ren, Wenjian Wang

Abstract: With the wide application of service technologies in various fields, the number of services is increasing dramatically. So, a function is often provided by many services of different QoS (quality of service). Thus, due to the lack of professional knowledge, users often face great difficulties in finding the right services they really need. Hence, accurate and efficient service recommendation is not just an effective way of service advertising, but also an important means to promote user experience. Most of traditional service recommendation methods are based on predictions of QoS values. However, because of the dynamic nature of the Internet, it is hard to guarantee the predicted values are consistent with the actual values. This article proposes a granule distribution-aware SVM (support vector machine) model for service recommendation, namely GDSVM4SR. It takes advantages of granular computing to identify similar users, refine the training service set, and decrease the influence of unfaithful ratings. And then, GDSVM4SR trains an SVM separating hyperplane to sort unknown services and generate recommendations, thus it can avoid the prediction of QoS values. Experimental results show that the proposed GDSVM4SR outperforms several state-of-the-art methods in terms of the efficiency and the precision of recommendation.