User Behavioral Context-Aware Service Recommendation for Personalized Mashups in Pervasive Environments

摘要

With the rapid development of mobile Internet and increasing amount of smart devices, Internet services have been integrated into peoples' daily lives. Due to the features of end-user-oriented mashups in pervasive environments, new challenges have been presented to conventional mashup approaches, including the complexity of user behaviors, the difficulty of predicting real-time user preference and other dynamic contexts. In this paper, we propose a new paradigm for behavioral context-based personalized mashup provision in pervasive environments by integrating mashup construction and execution into user natural behaviors. In the proposed paradigm, users with similar behavior patterns are identified and then probability distributions of potential behavior selection for user clusters are discovered from historical mashup logs, which provide supports for predicting and recommending user activities for future mashup constructions. Analysis and experiments indicate that our approach can effectively simplify personalized mashup composition, as well as improve the quality of mashup composition and recommendation based on behavioral contexts and personalization in pervasive environments.