Ph.D. Defense of Paul Lagrée
Thursday, October 12th at 2:00 pm, Room 435

Adaptive Methods for User-Centric Information Access Applications

Paul Lagrée.
Date and time
Thursday, October 12th 2017 at 2:00 pm.
ORI -- Room 435
Jury members
  • Ludovic Denoyer (UPMC Université Paris 6, France),
  • Pierre Senellart (École Normale Supérieure, France).
  • Aurélien Garivier (Université Paul Sabatier, France),
  • Stratis Ioannidis (Northeastern University, USA),
  • Themis Palpanas (Université Paris Descartes, France),
  • Fabian Suchanek (Télécom ParisTech, France).

The presentation will be held in english.


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.

Page maintenue par le webmaster - 11 octobre 2017
© Télécom ParisTech/IDS 2017