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.
Original language | English |
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Title of host publication | RecSys'08 |
Subtitle of host publication | Proceedings of the 2008 ACM Conference on Recommender Systems |
Pages | 171-178 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 1 Dec 2008 |
Externally published | Yes |
Event | ACM International Conference on Recommender Systems 2008 - Lausanne, Switzerland Duration: 23 Oct 2008 → 25 Oct 2008 Conference number: 2nd |
Publication series
Name | RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems |
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Conference
Conference | ACM International Conference on Recommender Systems 2008 |
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Abbreviated title | RecSys'08 |
Country/Territory | Switzerland |
City | Lausanne |
Period | 23/10/08 → 25/10/08 |
Keywords
- Decision guidance
- Decision optimization
- Development framework
- Preference learning
- Ranking
- Recommender systems