A Time-Aware and Data Sparsity Tolerant Approach for Web Service Recommendation

With the incessant growth of Web services on the Internet, designing effective Web service recommendation technologies based on Quality of Service (QoS) is becoming more and more important. Neighborhood-based Collaborative Filtering has been widely used for Web service recommendation, in which similarity measurement and QoS prediction are two key steps. However, traditional similarity models and QoS prediction methods rarely consider the influence of time information, which is an important factor affecting the QoS of Web services. Furthermore, traditional similarity models fail to capture the actual relationships between users or services due to data sparsity. These shortcomings seriously devalue the performance of neighborhood-based Collaborative Filtering. In order to make high-quality Web service recommendation, we propose a novel time-aware approach, which integrates time information into both the similarity measurement and the final QoS prediction. Additionally, in order to alleviate the data sparsity problem, a hybrid personalized random walk algorithm is employed to infer more indirect user similarities and service similarities. Finally, we conduct series of experiments to validate the effectiveness of our approaches.