Making Recommender Systems Work for Organizations
Nathalie Glance, Damian Arregui, Manfred Dardenne
For the past two years, we have been investigating the use of recommender systems as a technology in
support of knowledge sharing in organizations. Recommender systems are a way of extending the natural
process of recommendation by word-of-mouth to networked groups of people. They are able to provide
personalized reocmmendations that take into account similarities between people based on their user profiles.
The community around recommender systems that has emerged in the past five or so years has focused on
mehtods for constructing and learning user profiles, the exploration and testing of various recommendation
algorithms and the design of user interfaces, with applications primarily in the domains of electronic commerce
and leisure/entertainment. Thus far, we have focused our research in two areas: adapting recommendation
algorithms and user profile construction methods to take into account prior information regarding the existing
organizational social network; and addressing the incentive issues surrounding the use of a recommender
system for knowledge sharing in a organization. In this paper, we describe principally the incentive issues that
we have identified and how we have attempted toa lleviate them. We also report and analyze results from an
internal year-long trial of our recommender tool, the Knowledge Pump.
Proceedings of PAAM'99, London, UK, April 19-21, 1999.
kp-paam99.ps (1.26 MB)
paam99-kp.doc (710.00 kB)