PolyLens: a recommender system for groups of users

  • Authors:

  • Mark O'Connor;Dan Cosley;Joseph A. Konstan;John Riedl

  • Affiliations:

  • Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN

  • Venue:
  • ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email