Evaluation of Web Service Recommendation Performance via Sparsity Alleviating by Specificity-Aware Ontology-Based Clustering

With the development of information technology, considerable web services are published on the Internet rapidly in the last few years. It becomes a challenging task to recommend applicable web services to users and service recommendation becomes an influential approach to guide users to discover suitable services. In this situation, Collaborative Filtering (CF) based on rating is one of the powerful approaches for service recommendation but suffers from the data sparsity and cold-start problems due to the insufficiency of user-service information. In this paper, we present a novel ontology-based clustering approach that based on the terms specificity and similarity to overcome those limitations. We alleviate the sparsity problem using this novel clustering approach and then service user similarity is calculated using a Pearson Correlation Coefficient (PCC) measurement. Finally, user rating is predicted based on the alleviated user ratings and PCC values and recommendation is based on these predictions. We evaluated in the several viewpoints based on our previous work and results show that our approach can successfully alleviate the sparsity and cold-start problems and works effectively by lower prediction error compared with existing approaches.