An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Evaluating product search and recommender systems for E-commerce environments
Electronic Commerce Research
A cross-cultural user evaluation of product recommender interfaces
Proceedings of the 2008 ACM conference on Recommender systems
Acceptance issues of personality-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Critiquing recommenders for public taste products
Proceedings of the third ACM conference on Recommender systems
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
User profiling vs. accuracy in recommender system user experience
Proceedings of the International Working Conference on Advanced Visual Interfaces
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
Workshop on recommendation utility evaluation: beyond RMSE -- RUE 2012
Proceedings of the sixth ACM conference on Recommender systems
Improving top-n recommendations with user consuming profiles
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Learning Rating Patterns for Top-N Recommendations
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Learning User Preference Patterns for Top-N Recommendations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Nontrivial landmark recommendation using geotagged photos
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
| Hi-index | 0.00 |
Several researchers suggest that the Recommendation Systems (RSs) that are the "best" according to statistical metrics might not be the most satisfactory for the user. We explored this issue through an empirical study that involved 210 users and considered 7 RSs using different recommender algorithms on the same dataset. We measured user's perceived quality of each RS, and compared these results against measures of statistical quality of the considered algorithms as they have been assessed by past studies in the field, highlighting some interesting results.