Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval
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
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Supporting travel decision making through personalized recommendation
Designing personalized user experiences in eCommerce
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Adaptive preference elicitation for top-K recommendation tasks using GAI-networks
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A multiagent knowledge-based recommender approach with truth maintenance
Proceedings of the 2007 ACM conference on Recommender systems
Conversational Case-Based Recommendations Exploiting a Structured Case Model
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
Automated debugging of recommender user interface descriptions
Applied Intelligence
Review: Personalizing recommendations for tourists
Telematics and Informatics
The adaptive web
Rapid development of knowledge-based conversational recommender applications with advisor suite
Journal of Web Engineering
Case-based recommender systems: a unifying view
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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This paper describes an approach to product recommendation that combines in a novel way content- and collaborative-based filtering techniques. The system helps the user to specify a query that filters out unwanted products in electronic catalogues (content-based). Moreover, if the query produces too many or no results, the system suggests useful query changes that save the gist of the original request. This process goes on iteratively till a reasonable number of products is selected. Then, the selected products are ranked exploiting a case base of recommendation sessions (collaborative-based). Among the user selected items the system ranks higher items that are similar to those selected by other users in similar sessions (twofold similarity). The approach has been applied to a web travel application and it has been evaluated with real users. The proposed approach: a) reduces dramatically the number of user queries, b) reduces the number of browsed products and c) the selected items are found first on the ranked list.