PROJECT TITLE:

Diversifying Web Service Recommendation Results via Exploring Service Usage History - 2015

ABSTRACT:

The last decade has witnessed a tremendous growth of Web services as a major technology for sharing information, computing resources, and programs on the Internet. With the increasing adoption and presence of Net services, design of novel approaches for effective Net service recommendation to satisfy users’ potential necessities has become of paramount importance. Existing Net service recommendation approaches mainly specialise in predicting missing QoS values of Net service candidates which are attention-grabbing to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that suggested Web services are independent to each different, that generally might not be true. Therefore, several similar or redundant Net services might exist in a very recommendation list. In this paper, we have a tendency to propose a novel Internet service recommendation approach incorporating a user’s potential QoS preferences and diversity feature of user interests on Web services. User’s interests and QoS preferences on Internet services are first mined by exploring the Internet service usage history. Then we compute numerous Net service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We have a tendency to additionally construct a Web service graph based on the purposeful similarity between Internet services. Finally, we tend to present an innovative diversity-aware Web service ranking algorithm to rank the Net service candidates based mostly on their scores, and diversity degrees derived from the Internet service graph. Extensive experiments are conducted primarily based on a true world Web service dataset, indicating that our proposed Internet service recommendation approach significantly improves the standard of the advice results compared with existing methods


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