LORIA

In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

Dans cet article, nous proposons une nouvelle approche de système de recommandation basée sur la poursuite de cible à l'aide des filtres de Kalman. Nous supposons que les utilisateurs et leurs consommations de télévision (films, séries, émissions, etc.) sont représentés par des vecteurs dans l'espace multidimensionnel des genres des contenus (action, aventure, divertissement, etc.). Connaissant cet espace, nous proposons un algorithme basé sur les filtres de Kalman pour poursuivre le profil de l'utilisateur et prédire ses positions futures dans l'espace multidimensionnel des genres. A partir de cette prédiction, nous construisons une nouvelle stratégie de recommandation de contenus.

In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

Le campus virtuel EURO-QLIO est une filière de formation thématique européenne de l'enseignement de la Qualité, de la Logistique Industrielle et de l'Organisation. Cette filière thématique est constituée d'une Licence et d'un Master à triple diplômation entre la France, la Roumanie et la Bulgarie. En tant que campus virtuel, EURO-QLIO a pour caractéristique remarquable de s'appuyer fortement sur une utilisation des outils issus des TICE (plateforme d'enseignement à distance et modules multimédia) pour une pédagogie mixte (à distance sur une plate forme collaborative et en présentiel avec des séances de regroupement). Une partie de la forma-tion est tuteurée à distance par une équipe pédagogique transnationale. Le complément et l'évaluation des formations sont effectués lors des séances de regroupement. L'internationalisation des diplômes se fait à travers la transposition des contenus dans les différentes langues ; la mutua-lisation des contacts indus...

In this paper, we assume that users and their consumptions of television programs are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track the user's profile and to foresee the best prediction of their future position in the recommendation space. The approach is tested on data coming from TV consumptions. Using these results, we derive a new strategy for web recommendation based on a control loop. To conclude, we propose a new users' monitoring approach.

Decision-making has evolved as an interesting research problem for decision community. We consider a decision problem that takes into account several criteria called Multiple Criteria Decision Making (MCDM) in an interactive application for adaptive execution. In this paper, we present a method for automatically weighting criteria generation based on users' traces. In our method, we suggest a process that contains all steps describing alternately what is necessary to prepare in order to weight all criteria. We propose a modified method using Naïve Bayes network to exploit the traces (the past of users), there will be used as information for estimating the score of criteria. Experimental results are presented to illustrate a full process and an automatic generation of weighting criteria by a set of values.

In order to help and support learning practices, the development of web-based Personal Learning Environments (PLE) is widely adopted. A PLE is a set of services customized by the student. Among these services, resource (either digital or human) recommendation is a crucial one. The paper briefly reviews existing approaches for recommending resources in PLE. Then it describes a novel approach implemented in the OP4L prototype. OP4L is combining Social Web presence data and semantic web technologies, based on an intensive use of ontological models to represent the learning context. Then the paper reports about qualitative studies that were conducted with students on the currently available version of the OP4L prototype. The aim of the study was to get students' feedbacks about new online presence services offered in a LMS.

■ RÉSUMÉ • La conception et la réalisation logicielle d'environnements personnels d'apprentissage repose sur la modélisation d'un ensemble de données variées et l'utilisation des technologies web disponibles. Cet article donne un bref aperçu des catégories de données le plus souvent utilisées pour personnaliser les environnements d'apprentissage ainsi que des traitements opérés. Il se focalise ensuite sur les approches à base de connaissance et sur l'utilisation de modèles ontologiques partagés pour assurer l'interopérabilité des données entre les services. Cette dernière approche est illustrée par la modélisation et l'implantation de la notion de présence en ligne sur la plate-forme OP4L ainsi que par les retours d'étudiants utilisateurs.

In order to help and support learning practices, the development of web-based Personal Learning Environments (PLE) is widely adopted. A PLE is a set of services customized by the student. Among these services, resource (either digital or human) recommendation is a crucial one. The paper briefly reviews existing approaches for recommending resources in PLE. Then it describes a novel approach implemented in the OP4L prototype. OP4L is combining Social Web presence data and semantic web technologies, based on an intensive use of ontological models to represent the learning context. Then the paper reports about qualitative studies that were conducted with students on the currently available version of the OP4L prototype. The aim of the study was to get students' feedbacks about new online presence services offered in a LMS.

A new method for designing a Kalman filter for linear discrete-time systems with unkown inputs is presented. The algorithm recently developed for stochastic singular systems is applied to obtain a linear estimation of the state and unkown inputs. The necessary and sufficient conditions for the existence and stability of the filter are derived and proved. An illustrative example is included.

A connection between permutations that avoid 4231 and a certain queueing discipline is established. It is proved that a more restrictive queueing discipline corresponds to avoiding both 4231 and 42513, and enumeration results for such permutations are given.

Two currently active strands of research on logics for multi-agent systems are dynamic epistemic logic, focusing on the epistemic consequences of actions, and logics of coalitional ability, focusing on what coalitions of agents can achieve by cooperating strategically. In this paper we bridge these topics by considering the question: "what can a coalition achieve by making public announcements?". We propose an extension of public announcement logic with constructs of the form G ϕ, where G is a group of agents, with the intuitive meaning that G can jointly execute a publicly observable action such that ϕ will be true afterwards. Actions here are taken to be truthful public announcements, but turn out also to include sequences of such joint actions as well as protocols with alternating actions by different agents, in response to the actions of others. We also study in detail the difference between 'knowing how' (knowing de re) and 'knowing that' (knowing de dicto) in our framework: both can elegantly be expressed in the single-agent case. We present several meta-logical properties of this Group Announcement Logic, including a sound and complete axiomatisation, expressivity and the complexity of model checking. The results are based on but greatly extend a part of .