GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 4th international conference on Knowledge capture
Users' (Dis)satisfaction with the personalTV application: Combining objective and subjective data
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
| Hi-index | 0.00 |
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.