GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CoBase: a scalable and extensible cooperative information system
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Database (2nd ed.): principles, programming, and performance
Database (2nd ed.): principles, programming, and performance
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Retrieval
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
User Modeling in Human–Computer Interaction
User Modeling and User-Adapted Interaction
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Acquiring Customers' Requirementsin Electronic Commerce
Artificial Intelligence Review
FLEX: A Tolerant and Cooperative User Interface to Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Case-based recommender systems
The Knowledge Engineering Review
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Travel Destination Recommendation Systems: Behavioural Foundations and Applications (Cabi Publishing)
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Intelligent Techniques for Web Personalization: IJCAI 2003 Workshop, ITWP 2003, Acapulco, Mexico, August 11, 2003, Revised Selected Papers (Lecture Notes ... / Lecture Notes in Artificial Intelligence)
Adapting the interaction state model in conversational recommender systems
Proceedings of the 10th international conference on Electronic commerce
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Improving Decision Quality Through Preference Relaxation
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
Technology Dominance in Complex Decision Making: The Case of Aided Credibility Assessment
Journal of Management Information Systems
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
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This article presents a new technology called interactive query management (IQM), designed for supporting flexible query management in decision support systems and recommender systems. IQM aims at guiding a user to refine a query to a structured repository of items when it fails to return a manageable set of products. Two failure conditions are considered here, when a query returns either too many products or no product at all. In the former case, IQM uses feature selection methods to suggest some features that, if used to further constrain the current query, would greatly reduce the result set size. In the latter case, the culprits of the failure are determined by a relaxation algorithm and explained to the user, enumerating the constraints that, if relaxed, would solve the "no results" problem. As a consequence, the user can understand the causes of the failure and decide what is the best query relaxation. After having presented IQM, we illustrate its empirical evaluation. We have conducted two types of experiments, with real users and offline simulations. Both validation procedures show that IQM can repair a large percentage of user queries and keep alive the human computer interaction until the user information goals are satisfied.