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Apprentissage de la représentation du style écrit, application à la recommandation d’articles d’actualité

Julien Hay 1, 2 
2 LaHDAK - Données et Connaissances Massives et Hétérogènes
LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, SDD - Science des Données
Abstract : User modeling is an essential step when it comes to recommending products and offering services automatically. Social networks are a rich and abundant resource of user data (e.g. shared links, posted messages) that allow to model their interests and preferences. In this thesis, we propose to exploit news articles shared on social networks in order to enrich existing models with a new textual feature: the writing style. This thesis, at the intersection of the fields of natural language processing and recommender systems, focuses on the representation learning of writing style and its application to news recommendation. As a first step, we propose a new representation learning method that aims to project any document into a reference stylometric space. The hypothesis being tested is that such a space can be generalized by a sufficiently large set of reference authors, and that the vector projections of the writings of a "new" author will be stylistically close to the writings of a consistent subset of these reference authors. In a second step, we propose to exploit the stylometric representation for news recommendation by combining it with other representations (e.g. topical, lexical, semantic). We seek to identify the most relevant and complementary characteristics that can allow a more relevant and better quality recommendation of articles. The hypothesis that motivated this work is that the reading choices of individuals are not only influenced by the content (e.g. the theme of news articles, the entities mentioned), but also by the form (i.e. the style that can, for example, be descriptive, satirical, composed of personal anecdotes, interviews). The experiments conducted show that not only does writing style play a role in individuals' reading preferences, but also that, when combined with other textual features, it increases the accuracy and quality of recommendations in terms of diversity, novelty and serendipity.
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Submitted on : Tuesday, November 9, 2021 - 11:09:12 AM
Last modification on : Friday, August 5, 2022 - 9:27:32 AM
Long-term archiving on: : Thursday, February 10, 2022 - 6:31:06 PM


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  • HAL Id : tel-03420487, version 1


Julien Hay. Apprentissage de la représentation du style écrit, application à la recommandation d’articles d’actualité. Apprentissage [cs.LG]. Université Paris-Saclay, 2021. Français. ⟨NNT : 2021UPASG010⟩. ⟨tel-03420487⟩



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