UCWW Semantic-Based Service Recommendation Framework

Context-aware recommendation systems make recommendations by adapting to user's specific situation, and thus by exploring both the user preferences and the environment. In this paper, we propose a context-aware service recommendation framework utilising semantic knowledge in the Ubiquitous Consumer Wireless World (UCWW). The main objective of the framework is to provide users with the 'best' service instances that match their dynamic, contextualised and personalised requirements and expectations, thereby aligning to the always best connected and best served (ABC&S) paradigm. In the proposed framework, services and their related attributes are modeled dynamically as a heterogeneous network, based on a given network schema. Then, profile kernels - referring to the minimal set of features describing the user preferences - are extracted to model the user profiles. Subsequently, a recommendation engine, considering both the user profiles and current context, is applied to recommend 'best' service instances to users.