Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Evaluating critiquing-based recommender agents
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A comparative study of compound critique generation in conversational recommender systems
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Eye-tracking product recommenders' usage
Proceedings of the fourth ACM conference on Recommender systems
Users' eye gaze pattern in organization-based recommender interfaces
Proceedings of the 16th international conference on Intelligent user interfaces
Enhancing recommendation diversity with organization interfaces
Proceedings of the 16th international conference on Intelligent user interfaces
Comparative evaluation of recommender system quality
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Consumer decision patterns through eye gaze analysis
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
ACM Transactions on Interactive Intelligent Systems (TiiS)
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
Critiquing-based recommenders do not require users to state all of their preferences upfront or rate a set of previously experienced products. Compared to other types of recommenders, they require relatively little user effort, especially initially, despite potential accuracy problems. On the other hand, they rely on a set of critiques to elicit users feedback in order to improve accuracy. Thus the better the critiques are, the more accurately and efficiently the system becomes in generating its recommendations. This method has been successfully applied to high-involvement products. However, it was never tested on public taste products such as music, films, perfumes, fashion goods or wine. Indeed our initial trial adapting traditional critiquing methods to this new domain led to unsatisfactory results. This has motivated us to develop a novel approach named "editorial picked critiques" (EPC) that accounts for users' needs for popularity information, editorial suggestions, as well as their needs for personalization and diversity. Through an empirical study, we demonstrate that EPC presents a viable recommender approach and is superior on several dimensions to critiques generated by data mining methods.