Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Adaptive Methods for User-Centric Information Access Applications

Starts on 01/10/2014
Advisor : CAUTIS, Bogdan

Funding : Contrat doctoral uniquement recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI - LaHDAK

Defended on 12/10/2017, committee :

Research activities :

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.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.