Diversity Aware Web Service Recommendation Using WS-QOS and Service Usage Factors

Over the past 10 years, it has been witnessed that tremendous growth of Web services as a major technology for sharing data, computing resources, and programs on the Web. With the drastic evolution, adoption and presence of Web services, design of novel approaches for effective Web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing Web service recommendation approaches mainly focus on predicting missing QoS values of Web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended Web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant Web services may exist in a recommendation list. In this project, we propose a novel Web Service recommendation approach incorporating a user’s potential QoS preferences and diversity feature of user interests on Web services. It can be achieved through the absolute evaluation of service usage factors which is going to act a primary key for traditional collaborative filtering algorithm. User’s interests and QoS preferences on Web services are first mined by exploring the Web service usage history. Then we compute scores of Web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a Web service graph based on the functional similarity between Web services. Finally, we present an innovative diversityaware Web service ranking algorithm to rank the Web service candidates based on their scores, and diversity degrees derived from the Web service graph. Our proposed Web service recommendation approach is found to be effective since it significantly improves the quality of the recommendation results compared with existing methods.