The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Integrating tradeoff support in product search tools for e-commerce sites
Proceedings of the 6th ACM conference on Electronic commerce
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Evaluating critiquing-based recommender agents
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Consumer Decision Making in Knowledge-Based Recommendation
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Consumer decision making in knowledge-based recommendation
Journal of Intelligent Information Systems
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user self-motivated critiquing facility can highly improve users' subjective perceptions such as their decision confidence and trusting intentions. In this paper, we continue to investigate how to further reduce users' objective decision effort (e.g. time consumption) in such system by increasing the critique prediction accuracy of the system-proposed critiques. By means of real user evaluation, we proved that a new hybrid critiquing system design that integrates the preference-based recommendations organization technique for critiques suggestion can effectively help to increase the proposed critiques' application frequency and significantly contribute to saving users' task time and interaction effort.