Building Recommendation Engines

Understand your data and user preferences to make intelligent, accurate, and profitable decisions

Building Recommendation Engines

Starting
Suresh Kumar Gorakala

1 customer reviews
Understand your data and user preferences to make intelligent, accurate, and profitable decisions
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RRP $49.99
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Book Details

ISBN 139781785884856
Paperback372 pages

Book Description

A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.

The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best.

During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book!

Table of Contents

Chapter 1: Introduction to Recommendation Engines
Recommendation engine definition
Need for recommender systems
Big data driving the recommender systems
Types of recommender systems
Evolution of recommender systems with technology
Summary
Chapter 2: Build Your First Recommendation Engine
Building our basic recommendation engine
Summary
Chapter 3: Recommendation Engines Explained
Evolution of recommendation engines
Nearest neighborhood-based recommendation engines
Content-based recommender systems
Context-aware recommender systems
Hybrid recommender systems
Model-based recommender systems
Summary
Chapter 4: Data Mining Techniques Used in Recommendation Engines
Neighbourhood-based techniques
Mathematic model techniques
Machine learning techniques
Clustering techniques
Dimensionality reduction
Vector space models
Evaluation techniques
Summary
Chapter 5: Building Collaborative Filtering Recommendation Engines
Installing the recommenderlab package in RStudio
Datasets available in the recommenderlab package
Exploring the dataset
Building user-based collaborative filtering with recommenderlab
Building an item-based recommender model
Collaborative filtering using Python
Data exploration
User-based collaborative filtering with the k-nearest neighbors
Item-based recommendations
Summary
Chapter 6: Building Personalized Recommendation Engines
Personalized recommender systems
Content-based recommender systems
Context-aware recommender systems
Summary
Chapter 7: Building Real-Time Recommendation Engines with Spark
About Spark 2.0
Collaborative filtering using Alternating Least Square
Model based recommender system using pyspark
MLlib recommendation engine module
The recommendation engine approach
Summary
Chapter 8: Building Real-Time Recommendations with Neo4j
Discerning different graph databases
Neo4j
Neo4j Windows installation
Installing Neo4j on the Linux platform
Building recommendation engines
Summary
Chapter 9: Building Scalable Recommendation Engines with Mahout
Mahout - a general introduction
Setting up Mahout
Core building blocks of Mahout
Item-based collaborative filtering
Evaluating collaborative filtering
Evaluating user-based recommenders
Evaluating item-based recommenders
SVD recommenders
Distributed recommendations using Mahout
The architecture for a scalable system
Summary
Chapter 10: What Next - The Future of Recommendation Engines
Future of recommendation engines
Phases of recommendation engines
Popular methodologies
Temporal aspects of recommendation engines
Summary

What You Will Learn

  • Build your first recommendation engine
  • Discover the tools needed to build recommendation engines
  • Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations
  • Create efficient decision-making systems that will ease your work
  • Familiarize yourself with machine learning algorithms in different frameworks
  • Master different versions of recommendation engines from practical code examples
  • Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others

Authors

Table of Contents

Chapter 1: Introduction to Recommendation Engines
Recommendation engine definition
Need for recommender systems
Big data driving the recommender systems
Types of recommender systems
Evolution of recommender systems with technology
Summary
Chapter 2: Build Your First Recommendation Engine
Building our basic recommendation engine
Summary
Chapter 3: Recommendation Engines Explained
Evolution of recommendation engines
Nearest neighborhood-based recommendation engines
Content-based recommender systems
Context-aware recommender systems
Hybrid recommender systems
Model-based recommender systems
Summary
Chapter 4: Data Mining Techniques Used in Recommendation Engines
Neighbourhood-based techniques
Mathematic model techniques
Machine learning techniques
Clustering techniques
Dimensionality reduction
Vector space models
Evaluation techniques
Summary
Chapter 5: Building Collaborative Filtering Recommendation Engines
Installing the recommenderlab package in RStudio
Datasets available in the recommenderlab package
Exploring the dataset
Building user-based collaborative filtering with recommenderlab
Building an item-based recommender model
Collaborative filtering using Python
Data exploration
User-based collaborative filtering with the k-nearest neighbors
Item-based recommendations
Summary
Chapter 6: Building Personalized Recommendation Engines
Personalized recommender systems
Content-based recommender systems
Context-aware recommender systems
Summary
Chapter 7: Building Real-Time Recommendation Engines with Spark
About Spark 2.0
Collaborative filtering using Alternating Least Square
Model based recommender system using pyspark
MLlib recommendation engine module
The recommendation engine approach
Summary
Chapter 8: Building Real-Time Recommendations with Neo4j
Discerning different graph databases
Neo4j
Neo4j Windows installation
Installing Neo4j on the Linux platform
Building recommendation engines
Summary
Chapter 9: Building Scalable Recommendation Engines with Mahout
Mahout - a general introduction
Setting up Mahout
Core building blocks of Mahout
Item-based collaborative filtering
Evaluating collaborative filtering
Evaluating user-based recommenders
Evaluating item-based recommenders
SVD recommenders
Distributed recommendations using Mahout
The architecture for a scalable system
Summary
Chapter 10: What Next - The Future of Recommendation Engines
Future of recommendation engines
Phases of recommendation engines
Popular methodologies
Temporal aspects of recommendation engines
Summary

Book Details

ISBN 139781785884856
Paperback372 pages
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