Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Evaluating compound critiquing recommenders: a real-user study
Proceedings of the 8th ACM conference on Electronic commerce
Computational Statistics & Data Analysis
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth 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
Experience-Based critiquing: reusing critiquing experiences to improve conversational recommendation
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
ReComment: towards critiquing-based recommendation with speech interaction
Proceedings of the 7th ACM conference on Recommender systems
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Conversational recommender systems allow users to learn and adapt their preferences according to concrete examples. Critiquing systems support such a conversational interaction style. Especially unit critiques offer a low cost feedback strategy for users in terms of the needed cognitive effort. In this paper we present an extension of the experience-based unit critiquing algorithm. The development of our new approach, which we call nearest neighbor compatibility critiquing, was aimed at increasing the efficiency of unit critiquing. We combine our new approach with existing critiquing strategies to ensemble-based variations and present the results of an empirical study that aimed at comparing the recommendation efficiency (in terms of the number of critiquing cycles) of ensemble-based solutions with individual critiquing algorithms.