Practical Recommender Systems

Practical Recommender Systems
By 作者: Kim Falk
ISBN-10 书号: 1617292702
ISBN-13 书号: 9781617292705
Edition 版本: 1
Release Finelybook 出版日期: 2019-02-02
pages 页数: (432 )

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!
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.
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.
What’s inside

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


Practical Recommender Systems

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