This thesis presents the results of a multidisciplinary research project (Agorantic) on Recommender Systems. The goal of this work was to propose new features that may render recommender systems (RS) more attractive than the existing ones. We also propose a new approach to and a reflection about evaluation. In designing the system, we wanted to address the following concerns: 1. People are getting used to receive recommendations. Nevertheless, after a few bad recommendations, users will not be convinced anymore by the RS. 2. Moreover, if these suggestions come without explanations, why people should trust it? 3. The fact that item perception and user tastes and moods vary over time is well known. Still, most recommender systems fail to offer the right level of “reactivity” that users are expecting, i.e. the ability to detect and to integrate changes in needs, preferences, popularity, etc. Suggesting a movie a week after its release might be too late. In the same vein, it could take only a few ratings to make an item go from not advisable to advisable, or the other way around. 4. Users might be interested in less popular items (in the ” long tail”) and want less systematic recommendations. To answer these key issues, we have designed a new semantic and adaptive recommender system (SARS) including three innovative features, namely Argumentation, Dynamic Adaptation and a Matching Algorithm. • Dynamic Adaptation: the system is updated in a continuous way, as each new review/rating is posted. (Chapter 4) • Argumentation: each recommendation relies on and comes along with some keywords, providing the reasons that led to that recommendation. This can be seen as a first step towards a more sophisticated argumentation. We believe that, by making users more responsible for their choices, it will prevent them from losing confidence in the system. (Chapter 5) • Matching Algorithm: allows less popular items to be recommended by applying a match- ing game to users and items preferences. (Chapter 6) The system should be sensed as less intrusive thanks to relevant arguments (well-chosen words) and less responsible to unsatisfaction of the customers. We have designed a new recommender system intending to provide textually well-argued recommendations in which the end user will have more elements to make a well-informed choice. Moreover, the system parameters are dynamically and continuously updated, in order to pro- vide recommendations and arguments in phase with the very recent past. We have included a semantic level, i.e words, terms and phrases as they are naturally expressed in reviews about items. We do not use tags or pre-determined lexicon. The performances of our system are comparable to the state of the art. In addition, the fact that it provides argumentations makes it even more attractive and could enhance customers loyalty