Recommender systems have been evaluated in many, often incomparable, ways. In this
article, we review the key decisions in evaluating collaborative filtering recommender
systems: the user tasks being evaluated, the types of analysis and datasets being used, the
ways in which prediction quality is measured, the evaluation of prediction attributes other
than quality, and the user-based evaluation of the system as a whole. In addition to
reviewing the evaluation strategies used by prior researchers, we present empirical results …

One of the potent personalization technologies powering the adaptive web is collaborative
filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the
opinions of other people. CF technology brings together the opinions of large interconnected
communities on the web, supporting filtering of substantial quantities of data. In this chapter
we introduce the core concepts of collaborative filtering, its primary uses for users of the
adaptive web, the theory and practice of CF algorithms, and design decisions regarding …