Hierarchical Composable Optimization of Web Pages

TL;DR

FAME is a flexible framework that enables scalable, hierarchical, and modular optimization of web pages through algorithmic experimentation, accommodating dynamic user interests and diverse stakeholder contributions.

Contribution

It introduces a hierarchical, composable approach to web page optimization that separates concerns and supports modular, collaborative media creation.

Findings

Supports dynamic content integration from multiple sources

Enables experimentation with page models and algorithms

Facilitates collaboration among diverse stakeholders

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.