Enhancing Skyline Computation with Collaborative Filtering Techniques for QoS-Based Web Services Selection

The tremendous growth in the amount of available web services raised many challenges in service computing and made the process of choosing the best service candidates an important challenge. Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria. Those web services are considered optimal as they are not dominated by any other point in the search space. However, the data sparsity and the looseness of the dominance relationship used in comparing services pose some issues as the size of the Skyline may be still too large. Recommendation systems can overcome the limitations of Skyline computation by suggesting to the user the most relevant services according to his preferences. In this paper, we propose a new approach using collaborative filtering techniques to recommend to the users the best services according to the submitted request. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.