The framework will undoubtedly be expanded to include future applications of recommender systems.
Also, the paper identifies research challenges in recommender systems for the data mining community. For implementers, the paper
provides a means of making choices among the available applications and technologies. An implementer can choose a moneymaking
goal, select the interfaces that will help achieve that goal, and pick an implementation technique that supports the goal within the
interface.
This paper differs from our earlier work (Schafer, Konstan, & Riedl, 1999) in several key ways. First, the examples have been
updated and expanded to better reflect the rapidly expanding field of recommender systems. Second, the taxonomy has been
modified and expanded to more accurately encompass all of the aspects of recommendation technology and to be appropriate for a
data mining audience. Third, the opportunities section has been expanded to feature additional ideas and to reflect the current state
of the field. Finally, several new sections have been added, including sections relating recommender systems to traditional
marketing techniques and a discussion of privacy concerns.