A Memory-based Collaborative Filtering Algorithm for Recommending Semantic Web Services

This paper focuses on the construction of collaborative filtering (CF) recommender systems for Web services. The main contribution of the proposed approach is to reduce the problems caused by sparse rating data - one of the main shortcomings of memory-base CF algorithms - using semantic markup of Web services. In the presented algorithm, the similarity between users is computed using the Pearson correlation coefficient, extended to consider also the ratings of users for similarity services. Likewise, to predict the rating a user would give to a target service, the algorithm considers the ratings of neighbor users for the target service and also for similar services. Experiments conducted to evaluate the algorithm show that our approach has a significant impact on the accuracy of the algorithm, particularly when rating data are sparse.