Recommender Systems (Information Systems, Web and Pervasive Computing)

Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales.

On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

Lecturer at the GERiiCO laboratory at University Lille 3, Gerald Kembellec specializes in information science and communication.

Professor of Documentary Engineering Chair of CNAM, Ghislaine Chartron is director of the National Institute of Science and Technical documentation.

Professor at the University Paris 8, Imad Saleh is the Paragraph laboratory director and director of the graduate school Cognition Language Interaction.