Practical Machine Learning: Innovations in Recommendation

Description

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 recommendersCollect user data that tracks user actions—rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques Copyright Table of Contents Chapter 1. Practical Machine Learning What’s a Person To Do? Making Recommendation Approachable Chapter 2. Careful Simplification Behavior, Co-occurrence, and Text Retrieval Design of a Simple Recommender Chapter 3. What I Do, Not What I Say Collecting Input Data Chapter 4. Co-occurrence and Recommendation How Apache Mahout Builds a Model Relevance Score Chapter 5. Deploy the Recommender What Is Apache Solr/Lucene? Why Use Apache Solr/Lucene to Deploy? What’s the Connection Between Solr and Co-occurrence Indicators? How the Recommender Works Two-Part Design Chapter 6. Example: Music Recommender Business Goal of the Music Machine Data Sources Recommendations at Scale A Peek Inside the Engine Using Search to Make the Recommendations Chapter 7. Making It Better Dithering Anti-flood When More Is More: Multimodal and Cross Recommendation Chapter 8. Lessons Learned Appendix A. Additional Resources Slides/Videos Blog Books Training Apache Mahout Open Source Project LucidWorks Elasticsearch About the Authors