Software Engineering for Self-Adaptive Systems: A Research Roadmap..
In: B. H. C. Cheng, R. de Lemos, H. Giese, P. Inverardi and J. Magee, editors,
Software Engineering for Self-Adaptive Systems, volume 5525, series Lecture Notes in Computer Science, pages 1-26.
Springer, 2009.
Betty H. C. Cheng, Rogério de Lemos, Holger Giese, Paola Inverardi, Jeff Magee, Jesper Andersson, Basil Becker, Nelly Bencomo, Yuriy Brun, Bojan Cukic, Giovanna Di Marzo Serugendo, Schahram Dustdar, Anthony Finkelstein, Cristina Gacek, Kurt Geihs, Vincenzo Grassi, Gabor Karsai, Holger M. Kienle, Jeff Kramer, Marin Litoiu, Sam Malek, Raffaela Mirandola, Hausi A. Müller, Sooyong Park, Mary Shaw, Matthias Tichy, Massimo Tivoli, Danny Weyns and Jon Whittle.
[doi]
[BibTeX]
CARD: a decision-guidance framework and application for recommending composite alternatives.
In:
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems, pages 171-178.
ACM, New York, NY, USA, 2008.
Alexander Brodsky, Sylvia Morgan Henshaw and Jon Whittle.
[doi]
[abstract]
[BibTeX]
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.