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Building a Recommender: Best Practices with Apache Mahout and Solr - Emerging Technologies 2014
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- 1 EMERGING TECHNOLOGIES FOR THE ENTERPRISE CONFERENCE
- 2 What Does ML Look Like?
- 3 Learn a New Language
- 4 What Do You See?
- 5 Human insight plays an important role
- 6 Typical Machine Learning Workflow
- 7 Recommendation: Widely Used Machine Learning
- 8 Get Useful Indicators from Behaviors
- 9 History Matrix: Users by Items
- 10 Co-occurrence Matrix: Items by Items
- 11 Indicator Matrix: Anomalous Co-Occurrence
- 12 Setting up Solr index: Item Meta-Data
- 13 Welcome to the Music Machine!!
- 14 Sample Music Log File Data
- 15 Offline Analysis
- 16 Internals of the Recommender Engine
- 17 Real-time recontmendations using MapR data platform
- 18 A Quick Simplification
- 19 Architectural Advantage
- 20 Better Long-Term Recommendations
- 21 Why Use Dithering?
- 22 Apache Mahout: Overview
- 23 Mahout and Scala
- 24 Mahout and Spark
- 25 Mahout and h2o
- 26 Roadmap: Apache Mahout 1.0
- 27 Join Apache Mahout
- 28 Learn about Apache Mahout
