Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Acquiring Customers' Requirementsin Electronic Commerce
Artificial Intelligence Review
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
FLEX: A Tolerant and Cooperative User Interface to Databases
IEEE Transactions on Knowledge and Data Engineering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Retrieval Failure and Recovery in Recommender Systems
Artificial Intelligence Review
Increasing user decision accuracy using suggestions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supporting product selection with query editing recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Utility-based decision support system for schedule optimization
Decision Support Systems
COOPERATIVE QUERY REWRITING FOR DECISION MAKING SUPPORT AND RECOMMENDER SYSTEMS
Applied Artificial Intelligence
Preference-based search with adaptive recommendations
AI Communications - Recommender Systems
Generating Objectives: Can Decision Makers Articulate What They Want?
Management Science
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental controlled relaxation of failing flexible queries
Journal of Intelligent Information Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Qualitative reasoning based on fuzzy relative orders of magnitude
IEEE Transactions on Fuzzy Systems
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In online shopping scenarios, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. Interactive decision aids are a potential solution to this problem. However, decision aids that filter a very large set of alternatives based on initial preferences may eliminate potentially valuable alternatives early in the decision process and possibly negatively impact decision quality. To address this issue we introduce a new kind of decision aid that enables consumers to consider high-quality alternatives they initially eliminated. We develop a model of such a decision aid and evaluate it on a set of 2650 car advertisements gathered from popular used car advertiser website. We discuss the potential impact of our decision aid on decision quality and consideration sets parameters, and give an overview of implications of our study for practitioners and researchers.