QoS-Centric Web service Recommendation with Personalized Diversity

Abstract With the popularity and widespread adoption of SOA (Service Orient Architecture), the number of Web services has increased exponentially on the Internet. Meanwhile, as the cost of Internet applications decreases, users tend to use online Web services for their daily business and software development needs. Under such a background, recommending desirable Web services to users which meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue owing to the QoS preference is usually hard to provide for users, i.e., QoS preference is uncertain. To solve the problem, some recent works try to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the former QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the user’s service invocation history and provides a Web service recommendation algorithm, named personalized DPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experiment results show that the proposed approach can provide personalized and diversified Web services on the premise of ensuring the overall accuracy of the recommendation results.