Recommendation systems: a probabilistic analysis

Abstract

A recommendation system tracks past actions of a group of users to make recommendations to individual members of the group. The growth of computer-mediated marketing and commerce has led to increased interest in such systems. We introduce a simple analytical framework for recommendation systems, including a basis for defining the utility of such a system. We perform probabilistic analyses of algorithmic methods within this framework. These analyses yield insights into how much utility can be derived from the memory of past actions and on how this memory can be exploited.