Hands-On Recommendation Systems with Python: Start building powerful and personalized,recommendation engines with Python
by: Rounak Banik
ISBN-10: 1788993756
ISBN-13: 9781788993753
Publication Date 出版日期: 2018-07-31
Print Length 页数: 146
Book Description
By finelybook
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix 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.
Contents
1: GETTING STARTED WITH RECOMMENDER SYSTEMS
2: MANIPULATING DATA WITH THE PANDAS LIBRARY
3: BUILDING AN IMDB TOP 250 CLONE WITH PANDAS
4: BUILDING CONTENT-BASED RECOMMENDERS
5: GETTING STARTED WITH DATA MINING TECHNIQUES
6: BUILDING COLLABORATIVE FILTERS
7: HYBRID RECOMMENDERS
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
Authors
Rounak Banik
Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee. He has worked as a software engineer at Parceed,a New York start-up,and Springboard,an EdTech start-up based in San Francisco and Bangalore. He has also served as a backend development instructor at Acadview,teaching Python and Django to around 35 college students from Delhi and Dehradun.
He is an alumni of Springboard’s data science career track. He has given talks at the SciPy India Conference and published popular tutorials on Kaggle and DataCamp.
