Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Communications of the ACM
Database Management Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
Seven pitfalls to avoid when running controlled experiments on the web
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Overlapping experiment infrastructure: more, better, faster experimentation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.