Ted Dunning & Ellen Friedman
Machine Learning is a critical tool used for gaining actionable insight, more accurate foresight, and relevant inferences into your ever-increasing amount of data. A widespread application of machine learning is the recommendation engine. Apache Mahout, a project to build scalable machine learning libraries, greatly simplifies the process of extracting recommendations and relationships from datasets.
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.
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Ted Dunning, Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for Apache Storm. He contributed to Mahout clustering, classification and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, he built fraud detection systems for ID Analytics (LifeLock) and he has issued 24 patents to date. Ted has a PhD in computing science from University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. Connect with Ted on Twitter at @ted_dunning
Ellen Friedman is a consultant and commentator, currently writing mainly about big data topics. She is a committer for the Apache Mahout project and a contributor to the Apache Drill project. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics including molecular biology, nontraditional inheritance, and oceanography. Ellen is on Twitter at @Ellen_Friedman