Personalizing the Museum Experience through Context-Aware Recommendations

Idir Benouaret , Dominique Lenne (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

The main objective of our work is to demonstrate how recommender systems can be deployed to enhance access to museum collections. Visitors of museums are often overwhelmed by the information available in the space they are exploring. Therefore, finding relevant artworks to see in a limited amount of time is a crucial task. In this paper, we present a recommender system for mobile devices that (1) adapts to the users profiles and (2) is sensitive to their context (location, time, expertise, etc.). Our system aims to improve the visitors' experience and help them build their tours on-site according to their preferences and constraints. We first describe our recommendation framework, which consists in a hybrid recommendation system. It combines a semantic approach for the representation of museum domain using ontologies and thesauruses with a semantically-enhanced collaborative filtering method. Then, we present a contextual post filtering that enables the generation of a highly personalized tour based on the physical environment, the location of the visitors and the time they want to spend in the museum.

Mots clés: Recommender systems; semantic web; Context; Cultural heritage

Domaines: Recherche d'information [cs.IR]; Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

Idir Benouaret, Dominique Lenne. Personalizing the Museum Experience through Context-Aware Recommendations. SMC 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, Oct 2015, Hong Kong China. ⟨10.1109/SMC.2015.139⟩. ⟨hal-01319275⟩