Contextual information retrieval based on users' interests prediction and their social relations

The emergence of smartphones has given mobile computing access to everyday reality. More specifically, the context modeling offers users an effective way to customize search results. In recent years, many social sites have embraced the notion of context in their search engine. Indeed, with the availability of mobile devices, these new mobile sites have the advantage of providing users with more relevant elements based on their current situations. In this paper, we introduce a new approach for users' interests prediction to enrich their queries and expand their social circles. This approach is based on the technique of associative classification in order to predict the interests of users, from DBPEDIA. These interests are well used with a dual objective: (i) the enrichment of users' mobile queries and (ii) the extension of the social circle of users through the discovery of communities combining the random walk technique and the FOAF ontology modeling. Our experimental evaluation shows that our approach improves the quality and the accuracy of research results and also allows to enrich the relationships between individuals of real social networks.