Connectionists: PhD Thesis : Towards hybrid and explainable recommender systems mixing content analysis and collaborative filterings, Project OLKI Lorraine Université d'Excellence, France

[Marianne Clausel]

*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.brun at univ-lorraine.fr
marianne.clausel at 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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