CARD: A decision-guidance framework and application for recommending composite alternatives

Alex Brodsky, Sylvia Morgan Henshaw, Jon Whittle

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

32 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationRecSys'08
Subtitle of host publicationProceedings of the 2008 ACM Conference on Recommender Systems
Number of pages8
Publication statusPublished - 1 Dec 2008
Externally publishedYes
EventACM International Conference on Recommender Systems 2008 - Lausanne, Switzerland
Duration: 23 Oct 200825 Oct 2008
Conference number: 2nd

Publication series

NameRecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems


ConferenceACM International Conference on Recommender Systems 2008
Abbreviated titleRecSys'08


  • Decision guidance
  • Decision optimization
  • Development framework
  • Preference learning
  • Ranking
  • Recommender systems

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