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Theses

Deep Natural Language Processing for User Representation

Abstract : The last decade has witnessed the impressive expansion of Deep Learning (DL) methods, both in academic research and the private sector. This success can be explained by the ability DL to model ever more complex entities. In particular, Representation Learning methods focus on building latent representations from heterogeneous data that are versatile and re-usable, namely in Natural Language Processing (NLP). In parallel, the ever-growing number of systems relying on user data brings its own lot of challenges. This work proposes methods to leverage the representation power of NLP in order to learn rich and versatile user representations.Firstly, we detail the works and domains associated with this thesis. We study Recommendation. We then go over recent NLP advances and how they can be applied to leverage user-generated texts, before detailing Generative models.Secondly, we present a Recommender System (RS) that is based on the combination of a traditional Matrix Factorization (MF) representation method and a sentiment analysis model. The association of those modules forms a dual model that is trained on user reviews for rating prediction. Experiments show that, on top of improving performances, the model allows us to better understand what the user is really interested in in a given item, as well as to provide explanations to the suggestions made.Finally, we introduce a new task-centered on UR: Professional Profile Learning. We thus propose an NLP-based framework, to learn and evaluate professional profiles on different tasks, including next job generation.
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Submitted on : Thursday, January 27, 2022 - 1:02:08 PM
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  • HAL Id : tel-03545621, version 1

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Clara Gainon de Forsan de Gabriac. Deep Natural Language Processing for User Representation. Artificial Intelligence [cs.AI]. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS274⟩. ⟨tel-03545621⟩

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