Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Conversational Case-Based Reasoning
Applied Intelligence
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Diverse Product Recommendations Using an Expressive Language for Case Retrieval
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint 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
Compound Critiques for Conversational Recommender Systems
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Generating Diverse Compound Critiques
Artificial Intelligence Review
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Increasing user decision accuracy using suggestions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International Journal of Electronic Commerce
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
Enhancing the diversity of conversational collaborative recommendations: a comparison
Artificial Intelligence Review
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
An Analysis of Research Themes in the CBR Conference Literature
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Evaluating preference-based search tools: a tale of two approaches
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the evaluation of dynamic critiquing: a large-scale user study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Jointly optimising relevance and diversity in image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Knowledge-Based Systems
The adaptive web
Eye-tracking product recommenders' usage
Proceedings of the fourth ACM conference on Recommender systems
Enhancing recommendation diversity with organization interfaces
Proceedings of the 16th international conference on Intelligent user interfaces
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
ACM Transactions on Internet Technology (TOIT)
The effect of context-aware recommendations on customer purchasing behavior and trust
Proceedings of the fifth ACM conference on Recommender systems
Ways of computing diverse collaborative recommendations
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-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
A hybrid similarity concept for browsing semi-structured product items
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Case-based recommender systems: a unifying view
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Improving the performance of recommender systems that use critiquing
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Qualitative vs. quantitative plan diversity in case-based planning
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
The benefits of opening recommendation to human interaction
BCS-HCI '11 Proceedings of the 25th BCS Conference on Human-Computer Interaction
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Reutilization of diagnostic cases by adaptation of knowledge models
Engineering Applications of Artificial Intelligence
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
In the past conversational recommender systems have adopted a similarity-based approach to recommendation, preferring cases that are similar to some user query or profile. Recent research, however, has indicated the importance of diversity as an additional selection constraint. In this paper we attempt to clarify the role of diversity in conversational recommender systems, highlighting the pitfalls of naively incorporating current diversity-enhancing techniques into existing recommender systems. Moreover, we describe and fully evaluate a powerful new diversity-enhancing technique that has the ability to significantly improve the performance of conversational recommender systems across the board.