GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
A comparison of two compound critiquing systems
Proceedings of the 12th international conference on Intelligent user interfaces
On the evaluation of dynamic critiquing: a large-scale user study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Knowledge-Based Systems
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
Improving the performance of unit critiquing
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
History-aware critiquing-based conversational recommendation
Proceedings of the 22nd international conference on World Wide Web companion
ReComment: towards critiquing-based recommendation with speech interaction
Proceedings of the 7th ACM conference on Recommender systems
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Product recommendation systems are now a key part of many e-commerce services and have proven to be a successful way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders, which permit users to tweak the features of recommended products in order to refine their needs and preferences. In this paper, we describe a novel approach to reusing past critiquing histories in order to improve overall recommendation efficiency.