1. Learning and inference engine applied to
ubiquitous recommender system
Djallel Bouneffouf
Institut Telecom, Telecom SudParis, CNRS UMR Samovar
9, rue Charles Fourier, 75004 Evry
Djallel.bouneffouf@it-sudparis.eu
The need for adapting information systems to the user context has been accentuated
by the extensive development of mobile applications that provide a considerable
amount of data of all types (images, texts, sounds, videos, etc.). It becomes thus
crucial to help users by guiding them in their access to information.
Systems should be able to recommend information helping the user to fulfill his/her
goal. The information given by the system depends on the user’s situation, i.e. an
instance of the context. Possible situations and the associated actions reflect the
user’s work habits.
Major difficulties when applying techniques to adapt a system to the user follow:
- Avoiding the intervention of experts: on one hand, experts are not sure of the
interest of the user, may define wrong ideas about him; on the other hand, an expert
is not always available [9, 26].
- Starting from scratch: in the initial state, the system’s behavior should not be
incoherent for the user to not refuse it quickly [7, 27, 29].
- A slow learning process: the learning process has to be quick to avoid bothering
the user with incorrect recommendation [11, 22, 23, 24].
-The evolution of the user’s interest: the interest of the user may change with the
time. The system has to be continuously adapted to this dynamic change using the
user’s context information to provide the relevant recommendations because, if the
system behavior is incoherent, the user refuses it quickly [9, 19, 31].
We sum up all of these problems in the following scenario.
“Knowing the high mobility of its employees and their dependencies to the
information contained in their corporate databases, the Nomalys company has
equipped all mobile phones with the “NS” application. This application allows
them to adapt to the nomadic life by consulting the company’s database from their
mobile phones.
Knowing the high mobility of its employees and their dependencies to the
information contained in their corporate databases, the Nomalys company has
equipped all mobile phones with the “NS” application. This application allows them
to adapt to the nomadic life by consulting the company’s database from their mobile
phones. Because of the diversity of jobs existing in the company, Nomalys decides
provide the application with a generic recommender system, which has to retrieve
the relevant information to users without any set of actions predefined by an expert.

2. Paul, John and Lauren are new employees of the company integrating different teams
of the company (marketing, commercial, and technique resp.).
Regarding their agendas, they have a meeting with clients in Paris at midday. When
they arrive at their meetings, the system should recommend them the relevant
information which would help them to better manage their meetings. The system
recommends to Paul the register of complaints, to John the register of factures and to
Lauren the technical registers.
To do these recommendations without the need of an expert and avoiding starting
from scratch, the recommender system has to infer them from the actions of the
user’s team.
The recommender system finds that Paul often opens the register of complaints two
hours before his meeting and not at the meeting. Moreover, John always tries to find
companies which are near and do the same work as the one he will visit the next day.
Using this knowledge, one month later, the system is able to recommend the register
of complaints to Paul two hours before his meeting; it also recommends John
companies which are near and do the same work as the one he will visit the next
day”.
To do these recommendations, the learning process of the system has to be quick and
has to follow the evolution user’s interest.
In summary, to solve the problem of the scenario, I study during my PHD thesis the
possibility to start with a predefined set of actions, not defined by an expert, but by
the user’s social group (in the scenario we talk about job teams) and adapts it
progressively to a particular user. This default behavior allows the system to be
ready-to-use and the learning is a lifelong process. Thus, the system will, at first, be
only acceptable to the user, and will, as time passes, give more and more satisfying
results.
8. Bibliographie
[Assad et al., 2007] Mark Assad, David Carmichael, Judy Kay et Bob Kummerfeld. «
PersonisAD : Distributed, Active, Scrutable Model Framework for Context-Aware Services ».
Dans Anthony La-Marca, Marc Langheinrich et Khai N. Truong, rédacteurs, Proceedings of
the 5th International Conference on Pervasive Computing, PERVASIVE 2007, tome 4480 de
Lecture Notes in Computer Science, pages 55 - 72. Springer, Toronto, Ontario, Canada, mai
2007.
[Godoy et Amandi, 2005] Daniela Godoy et Analia Amandi. « User proling for Web page
filtering », Internet Computing, IEEE, tome 9, n° 4,pages 56-64, juillet - aout 2005.
[Christopher, 2006] Christopher M. Bishop, « Pattern Recognition And Machine Learning»,
Springer, 2006.