WE CREATE RECOMMENDER SYSTEMS
Modeling users, Understanding context, Adapting recommendations, Improving interaction.
Research Area and Main Goals
Recommender systems are the common focus of the works conducted in the team, since its creation in 2008. KIWI has been the first team in the lab to work on recommender systems. The scientific focus is the automatic exploration of digital traces: logs, clickstreams, ratings, annotations, writing in blogs, etc. This exploration is based on models issued from machine learning, data mining, subjective logic, collaborative and content-based filtering, considering only traces or including human factors for their processing.
Our objective is to model the user behavior (descriptive modeling), explain it (diagnostic analysis), predict its evolution (predictive modeling) or determine what actions to do to achieve a goal (prescriptive modeling). Our research topics include individual (user) and collective modeling (community), instant and dynamic modeling, single domain (cultural goods, educational resources) and cross-domain systems, personalization (adaptation to the user) and flexibility (adaptation to the context). Application domains were mainly e-education, e-health, digital media services, e-commerce, cultural heritage, information systems and social networks.