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Illustration de l'actualité : PhD defense Paul Lagrée : Adaptive Methods for User-Centric Information Access Applications

PhD defense Paul Lagrée : Adaptive Methods for User-Centric Information Access Applications

jeudi
12
octobre
2017

PhD Comics : I'm defending my thesis, Mom !

Jury

Rapporteurs

  • Ludovic Denoyer, UPMC Université Paris 6 (France)
  • Pierre Senellart, Ecole Normale Supérieure (France)

Examinateurs

  • Aurélien Garivier, Université Paul Sabatier (France)
  • Stratis Ioannidis, Northeastern University (Etats-Unis)
  • Themis Palpanas, Université Paris Descartes (France)
  • Fabian Suchanek, Télécom ParisTech (France)

Directeurs de thèse

  • Olivier Cappé, LIMSI (France)
  • Bogdan Cautis, Université Paris-Sud (France)

Abstract

When users interact on modern Web systems, they let numerous footprints which we propose to exploit in order to develop better applications for information access. We study a family of techniques centered on users, which take advantage of the many types of feedback to adapt and improve services provided to users. The first part of this thesis is dedicated to an approach for as-you-type search on social media. The problem consists in retrieving a set of k search results in a social-aware environment under the constraint that the query may be incomplete (e.g., if the last term is a prefix). We adopt a "network-aware" interpretation of information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. Then, we study a generic version of influence maximization, in which we want to maximize the influence of marketing or information campaigns by adaptively selecting "spread seeds" from a small subset of the population. Finally, we propose to address the well-known cold start problem faced by recommender systems with an adaptive approach. We introduce a bandit algorithm that aims to intelligently achieve the balance between "bad" and "good" recommendations.