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The scientific literature is a large information network linking various actors (laboratories, companies, institutions, etc.). The vast amount of data generated by this network constitutes a dynamic attributed heterogeneous network, in which new information is constantly produced and from which it is increasingly difficult to extract content of interest. In this article, I present my first thesis works in partnership with an industrial company. This later offers a scientific watch tool addressing various issues, such as the recommendation of articles and the search for experts. First, I detail the data usually associated with the scientific literature. Then I present the problem of expert finding, its evaluation and I suggest a new method to juge the quality of an algorithm. Finally, I introduce a representation learning algorithm for the problem of embedding the nodes of a graph in a vector space of small dimension, extended to integrate the textual information related to these nodes. MOTS-CLÉS : littérature scientifique, système de recommandation, recherche d’information

Apprentissage de Représentation appliqué à la Recommandation pour la Littérature Scientifique.