2012 HierarchicalComposableOptimizat

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Subject Headings: Multivariate Testing, Web Page Optimization.

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Abstract

The process of creating modern Web media experiences is challenged by the need to adapt the content and presentation choices to dynamic real-time fluctuations of user interest across multiple audiences. We introduce FAME -- a Framework for Agile Media Experiences -- which addresses this scalability problem. FAME allows media creators to define abstract page models that are subsequently transformed into real experiences through algorithmic experimentation. FAME's page models are hierarchically composed of simple building blocks, mirroring the structure of most Web pages. They are resolved into concrete page instances by pluggable algorithms which optimize the pages for specific business goals. Our framework allows retrieving dynamic content from multiple sources, defining the experimentation's degrees of freedom, and constraining the algorithmic choices. It offers an effective separation of concerns in the media creation process, enabling multiple stakeholders with profoundly different skills to apply their crafts and perform their duties independently, composing and reusing each other's work in modular ways.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2012 HierarchicalComposableOptimizatRonen Barenboim
Edward Bortnikov
Nadav Golbandi
Amit Kagian
Liran Katzir
Ronny Lempel
Hayim Makabee
Scott Roy
Oren Somekh
Hierarchical Composable Optimization of Web Pages10.1145/2187980.21879872012