Diversifying Web Service Recommendation Results via Exploring Service Usage History
The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing 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 paper, we propose a novel web 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 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 diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.
G. Kang et al., "Diversifying Web Service Recommendation Results via Exploring Service Usage History," IEEE Transactions on Services Computing, vol. 9, no. 4, pp. 566-579, Institute of Electrical and Electronics Engineers (IEEE), Jul 2016.
The definitive version is available at https://doi.org/10.1109/TSC.2015.2415807
Keywords and Phrases
Collaborative filtering; Graphic methods; Websites; Computing resource; Content-based approach; Diversity; Functional similarity; Service usage; User interests; Web service ranking; Web service recommendations; Web services; QoS preference
International Standard Serial Number (ISSN)
Article - Journal
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01 Jul 2016