Practical Machine Learning: Innovations in Recommendations
by Ted Dunning and 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 settings and demonstrates how even a small-scale development team can design an effective large-scale recommender.
In 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