Who predicts better? - Results from an online study comparing humans and an online recommender system

Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, Joseph A Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Algorithmic recommender systems attempt to predict which items a target user will like based on information about the user's prior preferences and the preferences of a larger community. After more than a decade of widespread use, researchers and system users still debate whether such "impersonal" recommender systems actually perform as well as human recommenders. We compare the performance of MovieLens algorithmic predictions with the recommendations made, based on the same user profiles, by active MovieLens users. We found that algorithmic collaborative filtering outperformed humans on average, though some individuals outperformed the system substantially and humans on average outperformed the system on certain prediction tasks.

Original languageEnglish (US)
Title of host publicationRecSys'08
Subtitle of host publicationProceedings of the 2008 ACM Conference on Recommender Systems
Pages211-218
Number of pages8
DOIs
StatePublished - Dec 1 2008
Event2008 2nd ACM International Conference on Recommender Systems, RecSys'08 - Lausanne, Switzerland
Duration: Oct 23 2008Oct 25 2008

Publication series

NameRecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

Other

Other2008 2nd ACM International Conference on Recommender Systems, RecSys'08
CountrySwitzerland
CityLausanne
Period10/23/0810/25/08

Keywords

  • Human recommenders
  • MAE
  • MovieLens
  • Predictions
  • Recommender evaluation
  • Recommender systems

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    Krishnan, V., Narayanashetty, P. K., Nathan, M., Davies, R. T., & Konstan, J. A. (2008). Who predicts better? - Results from an online study comparing humans and an online recommender system. In RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems (pp. 211-218). (RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems). https://doi.org/10.1145/1454008.1454042