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
Table of Contents
- 1. Practical Machine Learning
- 2. Careful Simplification
- 3. What I Do, Not What I Say
- 4. Co-occurrence and Recommendation
- 5. Deploy the Recommender
- 6. Example: Music Recommender
- 7. Making It Better
- 8. Lessons Learned
- A. Additional Resources
- About the Authors
- Title: Practical Machine Learning: Innovations in Recommendation
- Release date: February 2014
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491915387