Yagmur Gizem Cinar - Sequence Prediction using Recurrent Neural Networks (RNNs) in the Context of Time Series and Information Retrieval Search Sessions

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Yagmur Gizem Cinar
Yagmur Gizem Cinar
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Jury :

  • Lynda Tamine-Lechani, professeur des universités, Université Paul Sabatier, rapporteur
  • Patrick Gallinari, professeur des universités, Sorbonne Université, rapporteur
  • Fabio Crestani, professeur, Università della Svizzera Italiana, examinateur
  • Vadim Strijov, professeur, Moscow Institute of Physics and Technology, examinateur
  • Ahlame Douzal, maître de conférences, Université Grenoble Alpes, examinateur
  • Julien Perez, Lead Researcher, Naver Labs Europe, invité
  • Eric Gaussier, PREX, Université Grenoble Alpes, directeur de thèse



This thesis investigates challenges of sequence prediction in different scenarios such as sequence prediction using recurrent neural networks (RNNs) in the context of time series and information retrieval (IR) search sessions. Predicting the unknown values that follow some previously observed values is basically called sequence prediction. It is widely applicable to many domains where a sequential behavior is observed in the data. In this study, we focus on two different types of sequence prediction tasks: time series forecasting and next query prediction in an information retrieval search session.