The main goal of this thesis resides in using rich and efficient profiling to improve the adequation between the retrieved information and the user's expectations. We focus on exploiting as much feedback as we can (being clicks, ratings or written reviews) as well as context. In the meantime, the tremendous growth of ubiquitous computing forces us to rethink the role of information access platforms. Therefore, we took interest not solely in performances but also in accompanying users through their access to the information.
Throughout this thesis, we focused on users dynamics modeling. Not only it improves the system performances but it also brings some kind of explicativity to the recommendation. Thus, we propose to accompany the user through his experience accessing information instead of constraining him to a given set of items the systems finds fitting.