- eBook:Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python
- Author:Rounak Banik
- Data:July 31, 2018
- Pages:146 pages
- Format:PDF, ePUB
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web
Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it’s friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.
This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you’ll get started with building and learning about recommenders as quickly as possible.
In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You’ll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques
With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you’re fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
What you will learn
- Get to grips with the different kinds of recommender systems
- Master data-wrangling techniques using the pandas library
- Building an IMDB Top 250 Clone
- Build a content based engine to recommend movies based on movie metadata
- Employ data-mining techniques used in building recommenders
- Build industry-standard collaborative filters using powerful algorithms
- Building Hybrid Recommenders that incorporate content based and collaborative fltering
Chapter 2: Manipulating Data with the Pandas Library
Chapter 3: Building an IMDB Top 250 Clone with Pandas
Chapter 4: Building Content-Based Recommenders
Chapter 5: Getting Started with Data Mining Techniques
Chapter 6: Building Collaborative Filters
Chapter 7: Hybrid Recommenders