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

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ACM Conference On Recommender Systems archive
Proceedings of the 2008 ACM conference on Recommender systems table of contents

Lausanne, Switzerland

POSTER SESSION: Posters table of contents

Pages 171-178

Year of Publication: 2008

ISBN:978-1-60558-093-7

Authors

Alexander Brodsky	George Mason University, Fairfax, VA, USA
Sylvia Morgan Henshaw	George Mason University, Fairfax, VA, USA
Jon Whittle	Lancaster University, Lancaster, United Kingdom

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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