Making Recommendations Better: An Analytic Model for Human-Recommender Interaction

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.

Read more at:
http://www.grouplens.org/papers/pdf/mcnee-chi06-hri.pdf