Practical Machine Learning: Innovations in Recommendation - Helion
ISBN: 978-14-919-1571-4
stron: 56, Format: ebook
Data wydania: 2014-08-18
Księgarnia: Helion
Cena książki: 67,92 zł (poprzednio: 78,98 zł)
Oszczędzasz: 14% (-11,06 zł)
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 simplification. You’ll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.
- Understand the tradeoffs between simple and complex recommenders
- Collect user data that tracks user actions—rather than their ratings
- Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis
- Use search technology to offer recommendations in real time, complete with item metadata
- Watch the recommender in action with a music service example
- Improve your recommender with dithering, multimodal recommendation, and other techniques
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Spis treści
Practical Machine Learning: Innovations in Recommendation eBook -- spis treści
- Practical Machine Learning
- 1. Practical Machine Learning
- Whats a Person To Do?
- Making Recommendation Approachable
- 2. Careful Simplification
- Behavior, Co-occurrence, and Text Retrieval
- Design of a Simple Recommender
- 3. What I Do, Not What I Say
- Collecting Input Data
- 4. Co-occurrence and Recommendation
- How Apache Mahout Builds a Model
- Relevance Score
- 5. Deploy the Recommender
- What Is Apache Solr/Lucene?
- Why Use Apache Solr/Lucene to Deploy?
- Whats the Connection Between Solr and Co-occurrence Indicators?
- How the Recommender Works
- Two-Part Design
- 6. Example: Music Recommender
- Business Goal of the Music Machine
- Data Sources
- Recommendations at Scale
- A Peek Inside the Engine
- Using Search to Make the Recommendations
- 7. Making It Better
- Dithering
- Anti-flood
- When More Is More: Multimodal and Cross Recommendation
- 8. Lessons Learned
- A. Additional Resources
- Slides/Videos
- Blog
- Books
- Training
- Apache Mahout Open Source Project
- LucidWorks
- Elasticsearch
- About the Authors
- Colophon
- Copyright