book

Practical Machine Learning: Innovations in Recommendation

by Ted Dunning, Ellen Friedman

February 2014

Intermediate to advanced

56 pages

59m

English

O'Reilly Media, Inc.

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What’s a Person To Do?Making Recommendation Approachable
Behavior, Co-occurrence, and Text RetrievalDesign of a Simple Recommender
Collecting Input Data
How Apache Mahout Builds a ModelRelevance Score
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 WorksTwo-Part Design
Business Goal of the Music MachineData SourcesRecommendations at ScaleA Peek Inside the EngineUsing Search to Make the Recommendations
DitheringAnti-floodWhen More Is More: Multimodal and Cross Recommendation
Slides/VideosBlogBooksTrainingApache Mahout Open Source ProjectLucidWorksElasticsearch

Content preview from Practical Machine Learning: Innovations in Recommendation

Appendix A. Additional Resources

Slides/Videos

October 2013 Strata + Hadoop World (New York) talk by Ted Dunning on building multimodal recommendation engine using search technology: http://slidesha.re/16juGjO
May 2014 Berlin Buzzwords video of “Multi-modal Recommendation Algorithms” talk by Ted Dunning: http://bit.ly/XXy2bm

Blog

Two related entries from Ted Dunning’s blog “Surprise and Coincidence”:

On recommendation, LLR, and a bit of code: http://bit.ly/1dCL5Vk
Software tutorials for corpus analysis: http://bit.ly/1dZdKyX

Books

Mahout in Action by Sean Owen, Robin Anil, Ted Dunning, and Ellen Friedman (Manning 2011): http://amzn.to/1eRFSbb
- Japanese translation: Mahout in Action (O’Reilly Japan): http://bit.ly/14td9DS
- Korean translation: Mahout in Action (Hanbit Media, Inc.): http://bit.ly/VzZHY9
Apache Mahout Cookbook by Piero Giacomelli (Packt Publishing 2013): http://amzn.to/1cCtQNP

Training

One-day technical course, “Machine Learning with Apache Mahout: Introduction to Scalable ML for Developers,” developed by the authors for MapR Technologies and co-developed by Tim Seears of Big Data Partnership. For details, see MapR or BDP.