Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems
Submitted by Sara on Fri, 2007-10-26 18:36.
| Publication Type | Conference Paper | |
| Year of Publication | 2006 | |
| Authors | McNee, S.M.; Riedl, J.; Konstan, J.A. | |
| Conference Name | Conference on Human Factors in Computing Systems | |
| Conference Location | Montréal, Québec, Canada | |
| Pagination | 1097-1101 | |
| Conference Start Date | 22/04/2006 | |
| Publisher | ACM | |
| ISBN Number | 1-59593-298-4 | |
| Abstract | Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems. | |
| URL | Click Here | |
| DOI | 1125451.1125659 | |