دکترا - کامپیوتر - یادگیری ماشین - گرونوبل - فرانسه - ۲۰۱۴
Learning with non-stationary data - application to collaborative filtering and link prediction between name entities in knowledge bases like freebase Subject: The continuous production of tremendous amount of data upsets the traditional view in science and information technology, particularly in machine learning (ML). These data evolve generally over time and, do not follow the fundamental hypothesis of stationarity upon which the learning theory is based. This is for example the case in collaborative filtering where the goal is to generate personalized recommendations for each user. Recommender systems filter out a potentially huge set of items, and extract a subset of N items that best matches user's needs with respect to other users preferences (observed) over existing items and who may have the same tastes than the latter. In this case, user preferences generally evolve over time ; as the perception of different items as well as their popularity are completely time d