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<p><b>General context</b><br>
</p>
<p> 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. <br>
<br>
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). <br>
<br>
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 <i>explainability</i>
of AI algorithms.<br>
<br>
In addition, the new European legislation on data protection
foresees to impose more <i>transparency</i> 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.<br>
<br>
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. <br>
<br>
</p>
<p><b>Scientific challenges and program</b><br>
The challenges are four fold :<br>
- <b>Definition</b>, in a quantitative way, of the concept of
transparency, and develop statistical methods to automatically
quantify the transparency degree of an algorithm. <br>
- <b>Classification</b> of recommender systems from the
literature, from the transparency point of view and/or robustness
degree with respect to missing data<br>
- <b>Conception</b> 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.<br>
- <b>Constitution </b>of data sets, allowing to evaluate
transparency of recommender systems<br>
<br>
</p>
<p><b>Application</b><br>
\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 <b>before 12 May
2018</b> in one single pdf to :<br>
<a class="moz-txt-link-abbreviated"
href="mailto:armelle.brun@univ-lorraine.fr">armelle.brun@univ-lorraine.fr</a><br>
<a class="moz-txt-link-abbreviated"
href="mailto:marianne.clausel@univ-lorraine.fr">marianne.clausel@univ-lorraine.fr</a><br>
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.<br>
</p>
<p><b>Practical informations</b><br>
<b>Duration: </b>3 years (full time position)<br>
<b>Starting date:</b> October, 2018<br>
</p>
<p><b>Supervisors</b><br>
A. Brun, University of Lorraine/LORIA, France, <a
class="moz-txt-link-freetext"
href="https://members.loria.fr/ABrun/">https://members.loria.fr/ABrun/</a><br>
M. Clausel, University of Lorraine/IECL, France, <a
class="moz-txt-link-freetext"
href="https://sites.google.com/site/marianneclausel/">https://sites.google.com/site/marianneclausel/</a><br>
<br>
<b>Working Environment</b><br>
</p>
<p>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. <br>
<br>
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...<br>
<br>
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. <br>
</p>
<b>Location</b> : Nancy, which is the capital of Lorraine in
France, with excellent train connection to Luxembourg (1h30) and
Paris (1h30).<br>
<b>Salary after taxes: </b>around 1600 euros.
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