3 S.M. McNee J. Riedl J.A. Konstan 2006 ACM SIGCHI Conference on Human Factors in Computing Systems Montréal, Québec, Canada ACM 1103-1108 28/04/2007 1-59593-298-4 <p>Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.</p> http://www.grouplens.org/papers/pdf/mcnee-chi06-hri.pdf