Recommender Systems: Kembellec/Recommender Systems
1674 Citations•2014•
Gérald Kembellec, G. Chartron, Imad Saleh
journal unavailable
This work deals with the understanding of the underlying models for recommender systems and describes their historical perspective, and analyzes their development in the content offerings and their impact on user behavior.
Abstract
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.