GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
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
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
IEEE Transactions on Knowledge and Data Engineering
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Breaking out of the box of recommendations: from items to packages
Proceedings of the fourth ACM conference on Recommender systems
On the complexity of package recommendation problems
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Composite retrieval of heterogeneous web search
Proceedings of the 23rd international conference on World wide web
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This paper proposes a framework for Composite Alternative Recommendation Development (CARD), which supports composite product and service definitions, top-k decision optimization, and dynamic preference learning. Composite services are characterized by a set of sub-services, which, in turn, can be composite or atomic. Each atomic and composite service is associated with metrics, such as cost, duration, and enjoyment ranking. The framework is based on the Composite Recommender Knowledge Base, which is composed of views, including Service Metric Views that specify services and their metrics; Recommendation Views that specify the ranking definition to balance optimality and diversity; parametric Transformers that specify how service metrics are defined in terms of metrics of its subservices; and learning sets from which the unknown parameters in the transformers are iteratively learned. Also introduced in the paper is the top-k selection criterion that, based on a vector of utility metrics, provides the balance between the optimality of individual metrics and the diversity of recommendations. To exemplify the framework, specific views are developed for a travel package recommender system.