Practical Machine Learning: Innovations in Recommendation
Publisher: O'Reilly
Released: August 2014

Building a simple but powerful recommendation system is much easier than you think. 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 recommender. The style of the report makes this subject approachable for all levels of expertise.

Authors 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 Apache Mahout library, and then easily deploy the recommender using search technology with Apache Solr. This powerful and effective combination is efficient: it 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 Mahout for 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

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