Personalized Information Retrieval (PIR) exploits the user's data in order to refine the retrieval, like for instance when users with different backgrounds may express different information needs with the same query. However, this additional source of information is not supported by the classical Information Retrieval (IR) process. In order to overcome this limit, we propose to generate the user profile out from his profile and social data. Then, we introduce several Personalized Information Retrieval models which integrate this profile at the querying step, allowing to personalize the search results. We study several combinations of the initial user's query with his profile. Furthermore, we present a PIR test collection that we built from the social bookmarking network Delicious, in order to evaluate our PIR models. Our experiments showed that the PIR models improve the retrieval results.
['Chahrazed Bouhini', 'Mathias Géry', 'Christine Largeron']
Personalized information retrieval models integrating the user's profile