Building a Recommendation Engine with Scala > PACKT 원서리스트

1: Introduction to Scala and Machine Learning
Setting up Scala, SBT, and Apache Spark
A quick introduction to Scala
Machine learning and recommendation engines
Summary

2: Data Processing Pipeline Using Scala
Entree a sample dataset for recommendation systems
ETL extract transform load
Extraction and transformation for machine learning
Setting up MongoDB and Apache Kafka
Data processing pipeline for Entree
Summary

3: Conceptualizing an E-Commerce Store
Importance of recommender systems in e-commerce
Types of recommendation methods
The architecture of the project
Summary

4: Machine Learning Algorithms
Hands on with Spark/MLlib
Data types
Statistics
Feature extraction and transformation
Classification/regression
Clustering
Association analysis
Summary

5: Recommendation Engines and Where They Fit in?
Populating the Amazon dataset
Creating a web app with user/product pages
Adding recommendation pages
Summary

6: Collaborative Filtering versus Content-Based Recommendation Engines
Content-based recommendation
Content-based recommendation steps
Collaborative filtering based recommendation
What is ALS?
Content-based versus collaborative filtering
Summary

7: Enhancing the User Experience
Adding product search
Adding recommendation listings
Understanding recommendation behavior
Summary
8: Learning from User Feedback
Introducing PredictionIO
Unified recommender
Summary

Appendix A: Index