Practical Recommender Systems

Practical Recommender Systems

Computers & Technology
ISBN: 1617292702 Format: PDF Edition: 1 edition Date: February 2, 2019 Pages: 432 pages Language: English

Download Practical Recommender Systems


Download .PDF eBook

Book Description

Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application!

About the Technology
Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.

About the Book
Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows.

What's inside

  • How to collect and understand user behavior
  • Collaborative and content-based filtering
  • Machine learning algorithms
  • Real-world examples in Python

Content

PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS

Chapter 1. What is a recommender?
Chapter 2. User behavior and how to collect it
Chapter 3. Monitoring the system
Chapter 4. Ratings and how to calculate them
Chapter 5. Non-personalized recommendations
Chapter 6. The user (and content) who came in from the cold

PART 2 - RECOMMENDER ALGORITHMS
Chapter 7. Finding similarities among users and among content
Chapter 8. Collaborative filtering in the neighborhood
Chapter 9. Evaluating and testing your recommender
Chapter 10. Content-based filtering
Chapter 11. Finding hidden genres with matrix factorization
Chapter 12. Taking the best of all algorithms: implementing hybrid recommenders
Chapter 13. Ranking and learning to rank
Chapter 14. Future of recommender systems

Book cover


Practical Recommender Systems
В закладки

Dear users and students. The Book Practical Recommender Systems on our website it is presented for demonstration only. We do not store the files, If you like the book, please remove it and to buy a printed version of the book.

If You feel that this book is belong to you and you want to unpublish it, Please Contact us.

This site comply with DMCA digital copyright. We do not store files not owned by us, or without the permission of the owner. We also do not have links that lead to sites DMCA copyright infringement.



Comments (0)
ADD COMMENTS
Прокомментировать
reload, if the code cannot be seen
Trends in Practical Applications of Heterogeneous Multi-Agent Systems
F# for Machine Learning Essentials
Operating System Concepts
Event Processing in Action
Data Science from Scratch
Building Embedded Systems
Building a Recommendation System with R
Using R With Multivariate Statistics