[PDF.55ou] Practical Machine Learning: Innovations in Recommendation
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Practical Machine Learning: Innovations in Recommendation
Ted Dunning, Ellen Friedman
[PDF.tk04] Practical Machine Learning: Innovations in Recommendation
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| #2845643 in Books | 2014-10-06 | Original language:English | PDF # 1 | 9.02 x.12 x5.98l,.0 | File type: PDF | 56 pages||1 of 1 people found the following review helpful.| Good overview of building a recommender with Apache Lucene/Solr|By Customer|The author gives a quick and effective rundown of some of the architectural and design decisions you should make - centered on domain problem solving and a positive UX.
Also, the essay touches on the importance of generating recommendations that are holistically good, not just spot-on accura|About the Author|
|Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, ZooKeeper, and Drill projects and mentor for the Apache Storm, DataFu, Flink, and Optiq projects. He contributed to Mahout
Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, 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 recommendation system.
Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simpl...
You easily download any file type for your device.Practical Machine Learning: Innovations in Recommendation | Ted Dunning, Ellen Friedman.Not only was the story interesting, engaging and relatable, it also teaches lessons.
