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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.
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Citations
Cited by
Introduction to Recommender Systems Handbook
Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378
Content-based Recommender Systems: State of the Art and Trends
Lops, P., de Gemmis, M., & Semeraro, G. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_3 · Cited by 1,898
A user-centric evaluation framework for recommender systems
Pu, P., Chen, L., & Hu, R. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043962 · Cited by 850
Rank and relevance in novelty and diversity metrics for recommender systems
Vargas, S., & Castells, P. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043955 · Cited by 709
Internet Research in Psychology
Gosling, S. D., & Mason, W. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015321 · Cited by 458
Metadata
- Authors
- McNee, S. M., Riedl, J., & Konstan, J. A.
- Venue
- CHI '06 Extended Abstracts on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2006-04-21
- Pages
- 1097-1101
- OpenAlex
- W2049670925
- Cited by
- 1,066
- References
- 0
- FWCI
- 13.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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- McNee, S. M., Riedl, J., & Konstan, J. A. (2006). Being accurate is not enough: how accuracy metrics have hurt recommender systems. CHI '06 Extended Abstracts on Human Factors in Computing Systems. https://doi.org/10.1145/1125451.1125659
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