*Context*
Theapplication contextof this internship concern social network
analysis. The theoretical contextis thepreference queriesapplied tovery
large databases.
The concept of preference queries has been established in the database
community and was intensively studied in the last decade. These queries
have dual benefits. On the one hand, they allow to interpret accurately
the information needs of a given user. On the other hand, they
constitute an effective method to reduce very large datasets to a small
set of highly interesting results and to overcome the empty result set.
A query is personalized by applying related user preferences stored in
the user’s profile.
However, with the advent of social networks such as Facebook, Twitter,
Instagram, or more locally Breizbook, the user is no longer considered
as an individual entity, at least more only. In this context,the user
designatesan interconnected social entityandis the authorofsignificant
information flow.
*Scientific purpose *
Aftera literature review, the objective of thisthesis will
bethedevelopment ofa collaborative systemfor personalizinganalyzes
(/i.e./preference queries) based on profilesof a user group.
As a first step, this would requirethe determinationof the closestusers
of the current userin a given community (ring of friends on Breizbook,
professional network on LinkedIn,...). Thereforetheimplementation ofa
similarity measurefor comparinguser profilesis required. The idea is
todefine the notion ofsimilarityskylineof profile query defined by the
set of profiles of the target database that are the most similar to the
query in the sense of the similaritydominance relationdefined in [3].
The idea is to achieve a /d-dimensional/comparison between user profiles
in terms of /d/local distance (or similarity) measures and to retrieve
those profiles that are maximally similar in the sense of the /Pareto
dominance relation/.
A step of user matching will then be added upstream of preference
queries [7]. The preference integration and recommandation [5] remain
unchanged, butthey will takeas inputpreferences from differentabd
multiple users.
Then we have to assess the quality of returned profiles to the user.
Indeed, the relevance of the delivered information and its adaptation to
the users’ preferences are key factors for acceptance or rejection of
recommendation systems. It is therefore to determine whether calculated
results are consistent with expected ones. In this case, appropriate
quality criteria should be defined. They can be inspired by the work in
data quality [1, 4] but may also be defined by the system users
themselves. The last step is therefore to qualify the matching process
based on the quality of the results. This qualification could be based
on the recall and precision measures. If the user is not satisfied with
the results, he/she could start a new matching process using other
matching criteria or by changing the settings.
*Required skills*
The successful candidate will hold a Master’s Degree in Computer Science
(or equivalent) and demonstrate outstanding skills in the following
areas (or similar):
*
Preference modeling
*
Machine Learning
*
Graph theory
*
Linked Data
Strong programming skills and English proficiency are preferential
criteria. Interested applicants must prepare:
*
Detailed Resume
*
List of marks
*
Letter(s) of recommendation
*Application*
Applications should be sent (in pdf format) to Tassadit BOUADI
([email protected] <mailto:[email protected]>_) and Arnaud
MARTIN ([email protected] <mailto:[email protected]>_) before
Friday, June 24, 2016.
*Bibliographie*
[1]. D. Grigori, V. Peralta, and M. Bouzeghoub. Service retrieval based
on behavioral spécifications and quality requirements. In WilM.P. Aalst,
Boualem Benatallah, Fabio Casati, and Francisco Curbera, editors,
Business Process Management, volume 3649, pages 392–397. Springer Berlin
Heidelberg, 2005.
[2]. T. Hogg, Inferring preference correlations from social networks,
Electronic Commerce Research and Applications, Vol. 9, pp. 29‐37, 2010.
[3]. K. Abbaci, A. Hadjali, L. Lietard, and D. Rocacher. A similarity
skyline approach for Handling graph queries - a preliminary report. In
Proceedings of the 2011 IEEE 27th International Conference on Data
Engineering Workshops, ICDEW ’11, 2011.
[4]. L. Berti-Equille, I. Comyn-Wattiau, M. Cosquer, Z. Kedad, S.
Nugier, V. Peralta, S. Si-Saied Cherfi, and V. Thion-Goasdoué.
Assessment and analysis of information quality : a multidimensional
model and case studies. International Journal of Information Quality, 2
:300–323, 2011.
[5]. T. Bouadi, M-.O. Cordier, and R. Quiniou. Computing skyline
incrementally in response to online preference modification. T.
Large-Scale Data- and Knowledge-Centered Systems, 10 :34–59, 2013.
[6]. S. Dhamal and Y. Narahari. Scalable preference aggregation in
social networks. In First AAAI Conference on Human Computation and
Crowdsourcing, pages 42–50. AAAI, 2013.
[7]. F. Elarbi, T. Bouadi, A. Martin and B. Ben Yaghlane. Fusion de
préférences pour la détection de communautés dans les réseaux sociaux.
LFA 2015
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Pr. Arnaud MARTIN
Université de Rennes 1 / IUT de Lannion
UMR 6074 IRISA
Rue Edouard Branly BP 30219
22302 Lannion cedex, France
Phone: +(33)2.96.46.94.60
========================================
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