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

Published: 23 October 2008 Publication History

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.

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Cited By

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  • (2021)Automatic Collection Creation and RecommendationProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478865(633-638)Online publication date: 13-Sep-2021
  • (2018)Personalized and Diverse Task Composition in CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.275566030:1(128-141)Online publication date: 1-Jan-2018
  • (2017)Customizing Travel Packages with Interactive Composite Items2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2017.14(137-145)Online publication date: Oct-2017
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    cover image ACM Conferences
    RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
    October 2008
    348 pages
    ISBN:9781605580937
    DOI:10.1145/1454008
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    Published: 23 October 2008

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    Author Tags

    1. decision guidance
    2. decision optimization
    3. development framework
    4. preference learning
    5. ranking
    6. recommender systems

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    RecSys08: ACM Conference on Recommender Systems
    October 23 - 25, 2008
    Lausanne, Switzerland

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    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2021)Automatic Collection Creation and RecommendationProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478865(633-638)Online publication date: 13-Sep-2021
    • (2018)Personalized and Diverse Task Composition in CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.275566030:1(128-141)Online publication date: 1-Jan-2018
    • (2017)Customizing Travel Packages with Interactive Composite Items2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2017.14(137-145)Online publication date: Oct-2017
    • (2017)Recommending Diverse and Personalized Travel PackagesDatabase and Expert Systems Applications10.1007/978-3-319-64471-4_26(325-339)Online publication date: 2-Aug-2017
    • (2016)A Composite Recommendation System for Planning Tourist Visits2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0110(626-631)Online publication date: Oct-2016
    • (2016)Tailoring Group Package Recommendations to Large Heterogeneous Groups Based on Multi-criteria OptimizationProceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS)10.1109/HICSS.2016.194(1537-1546)Online publication date: 5-Jan-2016
    • (2016)Task Composition in Crowdsourcing2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2016.27(194-203)Online publication date: Oct-2016
    • (2016)Cross-Domain Tourist Service Recommendation Through Combinations of Explicit and Latent FeaturesAdvances in Services Computing10.1007/978-3-319-49178-3_7(92-105)Online publication date: 10-Nov-2016
    • (2015)Building Representative Composite ItemsProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806465(1421-1430)Online publication date: 17-Oct-2015
    • (2015)On recommendation problems beyond points of interestInformation Systems10.1016/j.is.2014.08.00248:C(64-88)Online publication date: 1-Mar-2015
    • Show More Cited By

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