@inproceedings{bd472344e04b407886d858e043d392e4,
title = "Being accurate is not enough: How accuracy metrics have hurt recommender systems",
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.",
keywords = "Collaborative filtering, Metrics, Personalization, Recommender systems",
author = "McNee, {Sean M.} and John Riedl and Konstan, {Joseph A.}",
year = "2006",
doi = "10.1145/1125451.1125659",
language = "English (US)",
isbn = "1595932984",
series = "Conference on Human Factors in Computing Systems - Proceedings",
pages = "1097--1101",
booktitle = "CHI'06 Extended Abstracts on Human Factors in Computing Systems, CHI EA'06",
note = "Conference on Human Factors in Computing Systems, CHI EA 2006 ; Conference date: 22-04-2006 Through 27-04-2006",
}