*General context*
Over the last twenty years, an increasing attention has been paid to
recommender systems, widely popularized by the Netflix Challenge. The
main goal of a recommender system is to provide some users, with
personalized products, taking into account their profile and preferences.
Recent challenges are about the recommendation of products very complex
to describe : jobs, partners... Their characteristics can mix
heterogeneous features: quantitative (as ratings) and/or qualitative (as
reviews).
Moreover, new questions are emerging about explainability of algorithms.
Nowadays, Artificial Intelligence algorithms are democratized in our
erveyday life, and consumers want to understand the decision resulting
from these algorithms (why this decision and not another one ?) as well
as quantify the importance of each factor (element) in the decision
process (which element is the most important/sensitive). They require
more /explainability/ of AI algorithms.
In addition, the new European legislation on data protection foresees to
impose more /transparency/ to Artificial Intelligence algorithm. The law
envisages to make compulsory the agreement of users for using personal
data, which will reduce the amount of data that can be collected about
users. The customer will also have to be informed about the way their
personal data is used. From the algorithms point of view, the decrease
of data will impact the quality of the recommmendations.
All these changes, will impact shortly and significantly the design of
algorithms. In this thesis, we aim at designing and implementing new
explainable and transparent recommender systems for complex products, in
the frame of data sparsity.
*Scientific challenges and program*
The challenges are four fold :
- *Definition*, in a quantitative way, of the concept of transparency,
and develop statistical methods to automatically quantify the
transparency degree of an algorithm.
- *Classification* of recommender systems from the literature, from the
transparency point of view and/or robustness degree with respect to
missing data
- *Conception* of new hybrid and explainable recommender systems, robust
to sparse data. The products being complex, the heterogeneous
descriptions of the products, as well as the multi-sources of
information, will be used to construct understandable explanation.
Especially, natural language processing, and hybrid (content/social)
approaches will be studied. The algorithms will also be able to quantify
the weights and the sensitivity of each factor in the final decision.
- *Constitution *of data sets, allowing to evaluate transparency of
recommender systems
*Application*
\noindent The application should include a brief description of research
interests and past experience, a CV, degrees and grades, a copy of
Master thesis (or a draft thereof), motivation letter (short but
pertinent to this call), relevant publications (if any), and other
relevant documents. Candidates are encouraged to provide letter(s) of
recommendation or contact information to reference persons. Please send
your application *before 12 May 2018* in one single pdf to :
armelle.b...@univ-lorraine.fr
marianne.clau...@univ-lorraine.fr
The application of the preselected candidates will be reviewed by the
Doctoral School IAEM of University of Lorraine in June 2018 for
completing the selection process.
*Practical informations*
*Duration: *3 years (full time position)
*Starting date:* October, 2018
*Supervisors*
A. Brun, University of Lorraine/LORIA, France,
https://members.loria.fr/ABrun/
M. Clausel, University of Lorraine/IECL, France,
https://sites.google.com/site/marianneclausel/
*Working Environment*
The PhD candidate will work between the Probability and Statistic team
of the IECL lab and the KIWI Team of the LORIA lab which are two leading
institutions, respectively in Mathematics and Computer Science in
France. The two labs are both located at Nancy, France on the same campus.
The Probability and Statistic team of IECL is working on
interdisciplinary projects involving probabilistic modeling and
inference methods, with a focus on many applications as textual datas,
biology, spatial datas...
The KIWI team of LORIA is a dynamic group working on recommender system
and connected scientific domains over 20 researchers (including PhD
students) and that covers several aspects of the subject from theory to
applications, including statistical learning, data-mining, and cognitive
science.
*Location* : Nancy, which is the capital of Lorraine in France, with
excellent train connection to Luxembourg (1h30) and Paris (1h30).
*Salary after taxes: *around 1600 euros.
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