Des textes communautaires à la recommandation

Abstract : The thesis is about the transformation of unstructured textual data in structured data in order to be used by a recommender system. Recommender systems can operate on two main types of data: content descriptors as metadata or tags (content-based filtering), and usage data as rates or visited Web pages for example (collaborative filtering). Other data exist on the Web which are not used yet. With the emergence of the Web 2.0, users share their feelings, opinions, experiences on products, personalities, movies, music, etc. (through comments for example). This textual data generated by users potentially represent rich sources of information which can supplement data exploited by recommender systems. The exploitation of this kind of data could open new paths in this burgeoning field. Our objective in this thesis is to generate matrices relevant for recommender systems. The underlying idea is to enrich a system for a beginner service, which has still few own users, then too little usage data, by information on other users on the Web. The thesis begins with a state of the art of automatic recommendation. Then, we present the recommender systems and the textual corpus used for experiments. The next chapter presents first experiments with the content-based filtering approach. The next part contains the state of the art of opinion mining. Finally, we describe experiments done with collaborative filtering approach using opinion classification.
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Submitted on : Tuesday, November 8, 2011 - 10:17:53 AM
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Damien Poirier. Des textes communautaires à la recommandation. Autre [cs.OH]. Université d'Orléans, 2011. Français. ⟨NNT : 2011ORLE2005⟩. ⟨tel-00597422v2⟩

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