Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques 1st ed. Edition
by Akshay Kulkarni，Adarsha Shivananda，Anoosh Kulkarni，V Adithya Krishnan(Author)
Publisher finelybook 出版社: Apress; 1st ed. edition (November 22, 2022)
Language 语言: English
Print Length 页数: 261 pages
ISBN-10: 1484289536
ISBN-13: 9781484289532
Book Description
By finelybook
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.
You’ll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.
By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.
What You Will Learn
Understand and implement different recommender systems techniques with Python
Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization
Build hybrid recommender systems that incorporate both content-based and collaborative filtering
Leverage machine learning, NLP, and deep learning for building recommender systems
Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
