Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs
Artificial Intelligence Review
Artificial Intelligence Review
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
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
Understanding and improving automated collaborative filtering systems
Understanding and improving automated collaborative filtering systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Increasing user decision accuracy using suggestions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Case-based recommender systems
The Knowledge Engineering Review
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A unified framework for recommending diverse and relevant queries
Proceedings of the 20th international conference on World wide web
User-oriented product search based on consumer values and lifestyles
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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In conversational collaborative recommender systems, user feedback influences the recommendations. We report mechanisms for enhancing the diversity of the recommendations made by collaborative recommenders. We focus on techniques for increasing diversity that rely on collaborative data only. In our experiments, we compare different mechanisms and show that, if recommendations are diverse, users find target items in many fewer recommendation cycles.