Bouneffouf Djallel

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My main research interest is in the old goal of Artificial Intelligence (AI) which is building autonomous system that can learn to be broadly competent in dynamic and uncertain environments. The field of reinforcement learning (RL) has focused on this goal, which is different from supervised learning in that correct input/output pairs are never presented. Further, there is a focus on online performance, which involves finding a balance between exploration (exploring action for increasing the knowledge about the environment) and exploitation (of current knowledge).

recommender system; machine learning; exploration/ mobile; machine learning; exploration/exploitation recommender system; context l’apprentissage automatique; l’adaptation des learning; inference engine; ubiquitous ;recommende evolution user intrest traitement du langage naturel système question réponse reconnaissance d‟inférence textuelle recherche d‟information inférence temporelle natural language processing recognizing of textual entailment information retrieval. question answering system temporal inference recommender system context-aware exploration exploitation bandit algorithm recommender systems hybrid-e-greedy

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Bouneffouf Djallel

Documents

Following the user’s interests in mobile context aware recommender systems

Hybrid-e-greedy for mobile context-aware recommender system

Rôle de l’inférence temporel dans la reconnaissance de l’inférence textuelle

Learning and inference engine applied to ubiquitous recommender system

« L’apprentissage automatique », une étape importante dans l’adaptation des systèmes d’information à l’utilisateur

A contextual bandit algorithm for mobile context-aware recommender system

Exploration exploitation trade off in mobile context-aware recommender systems

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Social Recommender Systems Tutorial - WWW 2011