The Big Promise of Recommender Systems


  • Francisco J. Martin BigML, Inc.
  • Justin Donaldson BigML, Inc.
  • Adam Ashenfelter BigML, Inc.
  • Marc Torrens Strands, Inc.
  • Rick Hangartner Strands, Inc.



Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users’ explicit and implicit preferences (most often with their permission). Frequently they provide relevant recommendations that increase the revenue or user engagement of the online services that operate them. However, when we evaluate the current generation of recommender systems from the point of view of the “recommendee,” we find that most recommender systems serve the goals of the business instead of their users’ interests. Thus we believe that the big promise of recommender systems has yet to be fulfilled. We foresee a third wave of recommender systems that act directly on behalf of their users across a range of domains instead of acting as a sales assistant. We also predict that such new recommender systems will better deal with information overload, take advantage of contextual clues from mobile devices, and utilize the vast information and computation stores available through cloud-computing services to maximize users’ long-term goals




How to Cite

Martin, F. J., Donaldson, J., Ashenfelter, A., Torrens, M., & Hangartner, R. (2011). The Big Promise of Recommender Systems. AI Magazine, 32(3), 19-27.