In this paper, we have surveyed the recommender applications used by several of the largest E-commerce companies. We identified
several design parameters and developed a taxonomy that classifies these applications by their inputs, output, recommendation
method, degree of personalization, and delivery method. Classifying the applications revealed a set of application models that reflect
the state of practice. We have also explored promising directions in recommender systems, including application ideas built on
innovative models that transcend current practice. Finally, in the appendix, we discuss some of the critical social acceptance issues
surrounding recommender applications in E-commerce including privacy and trust.