Ph.D
Group : Heterogeneous Modeling

Intégration du Web Social dans les systèmes de recommandation

Starts on 01/03/2014
Advisor : SEGHOUANI BENNACER, Nacéra
[QUERCINI Gianluca]

Funding : Convention industrielle de formation par la recherche
Affiliation : Centrale Supélec
Laboratory :

Defended on 19/12/2017, committee :

Research activities :

Abstract :
The social Web grows more and more and gives through the web, access to a wide variety of resources, like sharing sites such as del.icio.us, exchange messages as Twitter, or social networks with the professional purpose such as LinkedIn, or more generally for social purposes, such as Facebook and LiveJournal. Thus, the same individual can be registered and active on different social networks (potentially having different purposes), in which it publishes various information, which are constantly growing, such as its name, locality, communities, messages, various activities, etc. This information is important especially for applications seeking to know their users in order to better understand their needs, activities and interests. The objective of our research is to exploit essentially the textual resources extracted from the different social networks of the same individual in order to construct his characterizing profile, which can be exploited in particular by applications seeking to understand their users, such as recommendation systems. Given its international dimension, the content of the Web is inherently multilingual and intrinsically ambiguous, since individuals from different origin publish it in natural language in a free vocabulary and therefore the exploited textual resources are also multilingual and ambiguous. Nevertheless, we propose automatic, multilingual, and unsupervised approaches using Wikipedia to build an expanded profile for each user by aggregating information from its various social networks. In addition, we analyzed the correlation between user’s personality traits and his / her discovered interests, with a view to further characterizing it.

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.