Context factors in context-aware recommender systems

Zahra Vahidi Ferdousi (1) , Elsa Negre (1) , Dario Colazzo (1)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

With the rise in volume of data from various sources, we have an increasing need of recommender systems, which provide a data filtering to help users to find appropriate information. To satisfy even more users' needs, a new kind of recommender systems called context-aware recommender systems (CARS) integrate contextual information related to the user in their recommendation process. However there exists no unique definition for context. In this poster we propose a context representation for CARS, to improve upon previous propositions, which can be used for a large spectrum of applications.

Mots clés: recommender systems; big data

Domaines: Informatique [cs]

Zahra Vahidi Ferdousi, Elsa Negre, Dario Colazzo. Context factors in context-aware recommender systems. AISR 2017 : Atelier interdisciplinaire sur les systèmes de recommandation, May 2017, Paris, France. ⟨hal-01729327⟩