发帖
雷达卡
by:Ted Dunning, Ellen Friedman
http://www.oreilly.com/data/free/machinelearning.csp?intcmp=il-strata-free-product-lgen_machinelearning
- Print Length: 53 pages
- It`s a short e-book.
Building a simple but powerful recommendation system is much easier than you think. 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 recommender. The style of the report makes this subject approachable for all levels of expertise.
Authors 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 Apache Mahout library, and then easily deploy the recommender using search technology with Apache Solr. This powerful and effective combination is efficient: it 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 Mahout for co-occurrence analysis
- Use Solr 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
1. Practical Machine Learning
What’s 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 16
Relevance Score
5. Deploy the Recommender
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 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
Practical Machine Learning Innovations in Recommendation.pdf
(7.98 MB, 需要: 1 个论坛币)
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