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Practical Machine Learning:
Innovations in Recommendation


Ted Dunning & Ellen Friedman


Building a simple but powerful recommendation system is much easier than you think. This guide explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommender.

Practical Machine LearningIn this guide, Practical Machine Learning: Innovations in Recommendation, authors and Mahout committers Ted Dunning and Ellen Friedman shed light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.

Download this e-book and learn how to:

  • Understand the tradeoffs between simple and complex recommenders
  • 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 and deploy a recommendation engine at scale
  • Improve your recommender with dithering, multimodal recommendation algorithms, and other techniques

DOWNLOAD THE E-BOOK

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