Cheng, B. H. C.; de Lemos, R.; Giese, H.; Inverardi, P.; Magee, J.; Andersson, J.; Becker, B.; Bencomo, N.; Brun, Y.; Cukic, B.; Serugendo, G. D. M.; Dustdar, S.; Finkelstein, A.; Gacek, C.; Geihs, K.; Grassi, V.; Karsai, G.; Kienle, H. M.; Kramer, J.; Litoiu, M.; Malek, S.; Mirandola, R.; Müller, H. A.; Park, S.; Shaw, M.; Tichy, M.; Tivoli, M.; Weyns, D. & Whittle, J.: Software Engineering for Self-Adaptive Systems: A Research Roadmap.. In: Cheng, B. H. C.; de Lemos, R.; Giese, H.; Inverardi, P. & Magee, J. (Hrsg.): Software Engineering for Self-Adaptive Systems. Springer, 2009 (Lecture Notes in Computer Science 5525), S. 1-26
[Volltext]
@inproceedings{conf/dagstuhl/ChengLGIMABBBCSDFGGGKKKLMMMPSTTWW09,
author = {Cheng, Betty H. C. and de Lemos, Rogério and Giese, Holger and Inverardi, Paola and Magee, Jeff and Andersson, Jesper and Becker, Basil and Bencomo, Nelly and Brun, Yuriy and Cukic, Bojan and Serugendo, Giovanna Di Marzo and Dustdar, Schahram and Finkelstein, Anthony and Gacek, Cristina and Geihs, Kurt and Grassi, Vincenzo and Karsai, Gabor and Kienle, Holger M. and Kramer, Jeff and Litoiu, Marin and Malek, Sam and Mirandola, Raffaela and Müller, Hausi A. and Park, Sooyong and Shaw, Mary and Tichy, Matthias and Tivoli, Massimo and Weyns, Danny and Whittle, Jon},
title = {Software Engineering for Self-Adaptive Systems: A Research Roadmap.},
editor = {Cheng, Betty H. C. and de Lemos, Rogério and Giese, Holger and Inverardi, Paola and Magee, Jeff},
booktitle = {Software Engineering for Self-Adaptive Systems},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {2009},
volume = {5525},
pages = {1-26},
url = {http://dblp.uni-trier.de/db/conf/dagstuhl/adaptive2009.html#ChengLGIMABBBCSDFGGGKKKLMMMPSTTWW09},
isbn = {978-3-642-02160-2},
keywords = {itegpub, myown}
}
Brodsky, A.; Henshaw, S. M. & Whittle, J.: CARD: a decision-guidance framework and application for recommending composite alternatives. RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems. New York, NY, USA: ACM, 2008, S. 171-178
[Volltext]
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.
@inproceedings{brodsky2008card,
author = {Brodsky, Alexander and Henshaw, Sylvia Morgan and Whittle, Jon},
title = {CARD: a decision-guidance framework and application for recommending composite alternatives},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
publisher = {ACM},
address = {New York, NY, USA},
year = {2008},
pages = {171--178},
url = {http://portal.acm.org/citation.cfm?id=1454037},
doi = {10.1145/1454008.1454037},
isbn = {978-1-60558-093-7},
keywords = {card, recommender, framework, service},
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.}
}