UCWW semantic-based service recommendation framework

Abstract

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.