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 recommendersCollect user data that tracks user actions--rather than their ratingsPredict what a user wants based on behavior by others, using Mahout for co-occurrence analysisUse Solr 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