La prédiction des intérêts des utilisateurs pour la RI contextuelle et la recommandation d'amis dans un environnement mobile
This work addresses the need for personalized and context-aware services in mobile computing, offering incremental improvements to existing methods for social sites and mobile users.
The paper tackles the problem of improving contextual information retrieval and friend recommendation in mobile environments by predicting user interests from DBpedia and discovering communities using random walks, resulting in more relevant search results and recommendations based on users' current situations.
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 and even the recommended elements by limiting the data space. Moreover, in recent years, many social sites have embraced the notion of context in their recommendations. 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. Thus, we introduce a new approach of contextual IR in a mobile environment. We offer a hand, an approach called SA-IRI based on the prediction of users' interests, from DBpedia, given their current situations. This approach applies the technique of associative classification in order to enrich the users' queries. Secondly, we introduce an approach of communities discovering, called Foaf-A-Walk, combining the random walk technique and the Foaf modeling, for friend recommendation.