Building a simple but powerful recommendation system is much easier than
you think. Approachable for all levels of expertise, this report
explains innovations that make machine learning practical for business
production settings--and demonstrates how even a small-scale development
team can design an effective large-scale recommendation system.
Apache Mahout committers Ted Dunning and Ellen Friedman walk you through
a design that relies on careful simplification. You'll learn how to
collect the right data, analyze it with an algorithm from the Mahout
library, and then easily deploy the recommender using search technology,
such as Apache Solr or Elasticsearch. Powerful and effective, this
efficient combination does learning offline and delivers rapid response
recommendations in real time.
- Understand the tradeoffs between simple and complex recommenders
- Collect user data that tracks user actions--rather than their ratings
- Predict what a user wants based on behavior by others, using Mahoutfor
co-occurrence analysis
- Use search technology to offer recommendations in real time, complete
with item metadata
- Watch the recommender in action with a music service example
- Improve your recommender with dithering, multimodal recommendation,
and other techniques
