Twitter Recommendation Algorithm

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product-mixer/README.md

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+Product Mixer
+=============
+
+## Overview
+
+Product Mixer is a common service framework and set of libraries that make it easy to build,
+iterate on, and own product surface areas. It consists of:
+
+- **Core Libraries:** A set of libraries that enable you to build execution pipelines out of
+  reusable components. You define your logic in small, well-defined, reusable components and focus
+  on expressing the business logic you want to have. Then you can define easy to understand pipelines
+  that compose your components. Product Mixer handles the execution and monitoring of your pipelines
+  allowing you to focus on what really matters, your business logic.
+
+- **Service Framework:** A common service skeleton for teams to host their Product Mixer products.
+
+- **Component Library:** A shared library of components made by the Product Mixer Team, or
+  contributed by users. This enables you to both easily share the reusable components you make as well
+  as benefit from the work other teams have done by utilizing their shared components in the library.
+
+## Architecture
+
+The bulk of a Product Mixer can be broken down into Pipelines and Components. Components allow you
+to break business logic into separate, standardized, reusable, testable, and easily composable
+pieces, where each component has a well defined abstraction. Pipelines are essentially configuration
+files specifying which Components should be used and when. This makes it easy to understand how your
+code will execute while keeping it organized and structured in a maintainable way.
+
+Requests first go to Product Pipelines, which are used to select which Mixer Pipeline or
+Recommendation Pipeline to run for a given request. Each Mixer or Recommendation
+Pipeline may run multiple Candidate Pipelines to fetch candidates to include in the response.
+
+Mixer Pipelines combine the results of multiple heterogeneous Candidate Pipelines together
+(e.g. ads, tweets, users) while Recommendation Pipelines are used to score (via Scoring Pipelines)
+and rank the results of homogenous Candidate Pipelines so that the top ranked ones can be returned.
+These pipelines also marshall candidates into a domain object and then into a transport object
+to return to the caller.
+
+Candidate Pipelines fetch candidates from underlying Candidate Sources and perform some basic
+operations on the Candidates, such as filtering out unwanted candidates, applying decorations,
+and hydrating features.