Recommendation of Mobile Services Employing Semantics and Community Generated Data

The number of online services is growing dramatically. Nowadays they can be semantic or Web 2.0 based, for fixed or mobile device consumption, end-user or provider created, oriented on specific user groups, social networks, etc. Therefore, selection and recommendation of services for the end users on the basis of the service and user data becomes a challenge, and conventional keyword-based information retrieval are no longer sufficient. Here we present an approach for effective selection and recommendation of heterogeneous online services, combining natural language based information retrieval techniques and analysis of semantic annotation, community-generated Web 2.0 type content and location awareness data.