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Kim Falk

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Practical Recommender Systems 1st Edition, Kindle Edition

by Kim Falk (Author) Format: Kindle Edition

4.5 44 ratings

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Summary

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!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

About the Reader

Readers need intermediate programming and database skills.

About the Author

Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems.

Table of Contents

PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS
What is a recommender?
User behavior and how to collect it
Monitoring the system
Ratings and how to calculate them
Non-personalized recommendations
The user (and content) who came in from the cold
PART 2 - RECOMMENDER ALGORITHMS
Finding similarities among users and among content
Collaborative filtering in the neighborhood
Evaluating and testing your recommender
Content-based filtering
Finding hidden genres with matrix factorization
Taking the best of all algorithms: implementing hybrid recommenders
Ranking and learning to rank
Future of recommender systems

ISBN-13

978-1617292705
Edition

1st
Sticky notes

On Kindle Scribe
Publisher

Manning
Publication date

January 18, 2019
Language

English
File size

17718 KB
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Editorial Reviews

Review

"Covers the technical background and demonstrates implementations in clear and concise Python code."
-Andrew Collier, Exegetic
"Have you wondered how Amazon and Netflix learn your tastes in products and movies, and provide relevant recommendations? This book explains how it's done!" -Amit Lamba, Tech Overture
"Everything about recommender systems, from entry-level to advanced concepts" -Jaromir D.B. Němec, DB "A great and practical deep dive into recommender systems!"-Peter Hampton, Ulster University --This text refers to the paperback edition.

About the Author

Kim Falk is a Data Scientist at Adform, where he is working on recommender systems. He has experience in providing recommendations for large entertainment companies and working with big data solutions. --This text refers to the paperback edition.

Product details

ASIN ‏ : ‎ B09782BTD3
Publisher ‏ : ‎ Manning; 1st edition (January 18, 2019)
Publication date ‏ : ‎ January 18, 2019
Language ‏ : ‎ English
File size ‏ : ‎ 17718 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Sticky notes ‏ : ‎ On Kindle Scribe
Print length ‏ : ‎ 432 pages
Page numbers source ISBN ‏ : ‎ 1617292702

Best Sellers Rank: #675,783 in Kindle Store (See Top 100 in Kindle Store)
- #129 in Data Mining (Kindle Store)
- #237 in Information Technology
- #370 in Data Mining (Books)

Customer Reviews:
4.5 44 ratings

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Customer reviews

4.5 out of 5

44 global ratings

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Iuliia

Awesome book!

Reviewed in the United States on September 14, 2022

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This book is a real gem. I learn so much from it very quickly. It’s even better that an online courses on the same topic. Highly recommended!

One person found this helpful

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Mehrdad Alizadeh

What a well written book!

Reviewed in the United States on October 26, 2021

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This books give you a complete sense of how design a recommender system. It breaks down the steps and digs into details supported with figures and code! Great job Kim Falk!

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Ben

A very practical approach to Recommender Systems

Reviewed in the United States on July 26, 2019

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I like this book because of the practicality. I bought this book because it teaches you the whole flow of gathering data to all the way to recommending items to users. I wanted a book that paints this picture and it does it for me in a very simple and approachable way.

2 people found this helpful

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helipilot50

Clear, Educational and Practical

Reviewed in the United States on April 2, 2019

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This book is outstanding, it provides enough theory mixed with real-world examples and code to learn what is necessary to build and curate recommender systems. If you are a beginner, the book will help you learn. If you are an old dog, you can learn new tricks.

3 people found this helpful

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TheHives

Good for absolute beginners. Not amazing if you just want to learn the algorithms.

Reviewed in the United States on December 27, 2020

Disclaimer: I am just over half way through this book, and I expect to update this review when I have finished reading.

This book is divided into two parts. The first part, slightly less than half the book, introduces the topic, describes data collection, data monitoring, personalized vs. non-personalized recommendations, etc. This portion of the book wasn't very helpful for me personally, as I was already familiar with most of this material. However, if you're brand new to the topic, the author did a really fantastic job diving deep into what "kinds" of signals might be collected to build a recommender system. There are plenty of examples describing Amazon, Netflix, and more. The end result is that it should help the reader build a strong, intuitive sense of what kind of data must be collected in order to build a recommender, in addition to learning about instrumentation (collecting metrics) and why it's crucial.

Part 2 of the book goes into specific algorithms. There's some mathematical notation involved, but nothing too bad. I've only finished up to chapter 7 (similarities) and 8 (collaborative filtering) so far, though I found the information generally meaningful and easy to digest.

At times, the author does a great disservice. If you don't want to cover matrices in detail, that's fine. But to say on page 185: "Matrix is a fancy word for a table with numbers ..." Just.. wow. I understand this isn't the most mathematically rigorous book, but with these statements, the author is egregiously over simplifying important concepts and doing a great disservice to the reader.

Later on the same page when briefly mentioning pre-calculating item-item similarities, he states, that its "important when you’re talking about a catalog the size of Amazon (you can think about both the size of the Amazon River or Amazon the internet store!)." That last statement in the parenthesis – the author has a writing style where he keeps padding the length of this book with superfluous statements. When the author finally does get to a formula, he spends little time on the math or providing a detailed technical explanation. The details are hand waved. IMO, without these details, it's unlikely a serious practitioner can make use of the information in this book in a production use case.

The early chapters in this book ask the reader to pull down some Python Django application that works around the Movie Geeks dataset. I didn't do any of this work – it's really besides the point.

For the author of this book, my question is – WHO is your audience? If it's developers, are you trying to teach them django? Unless the reader is extremely familiar with the framework, it's just more cognitive load for the reader to figure out this tooling that you've unnecessarily imposed. It doesn't make it any easier to learn recommender systems. Any why a full web app? If the expected audience is a developer, you can explain the key concepts and leave all the django and web application portions out. Otherwise, you're just adding noise.

If I was the author, I would have provided crisp instructions on setting up a Jupyter notebook, installing Python 3 and any dependencies, and have the reader write all the code in Jupyter. And, use this as an opportunity to teach software developers about data science notebooks, hammer through the idea that building recommenders (or any kind of ML) is an iterative process. Despite how the code is laid out in Chapter 7, realistically, no one is ever going to build a Web front-end that also contains logic for calculating Pearson scores or measures the cosine distance. This logic would most likely be executed in a separate micro service or possibly in some offline/batch job.

8 people found this helpful

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Rupal Gupta

Really good introduction to recommender systems

Reviewed in the United States on March 27, 2021

It is an excellent book to get started on recommendation systems. A book I wish I had read before building my first recommendation system.
Ideas on association rules, weights for implicit ratings and a summary of landscape on recommender systems by big companies was particularly useful.

One person found this helpful

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Hexin Bai

Too easy for the graduate and yet no figure for the undergraduate

Reviewed in the United States on October 23, 2022

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I can see that it tries its best to avoid any mathematics. But in the same time, the author fails to give intuitive figure on the ideas such as manifold learning (P 286). Instead, the author puts in whole bunch of code which makes the book more like an doc rather than a serious book published. All in all, I can see that the author targets the undergraduate reader and yet fails to use the figure to explain the idea behind machine learning model. My suggestion is that: if you have a graduate level of math and statistics, then avoid this book. If you are an undergraduate who is about join a recsys team as an pure SDE in the company, then you may buy and take a few glimpse.

2 people found this helpful

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Ryan Caracciolo

Good Intent , Good Idea , Bad Execution

Reviewed in the United States on June 28, 2019

This books is predicated on using and standing up a web application... For a beginner, the GitHub documentation is bad. I give it 1 out of 5 stars simply because completing Chapter 1 is non-trivial.

3 people found this helpful

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Christian

Perfect mix of easy to understand and going into depth

Reviewed in Germany on July 3, 2021

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I often feel that machine learning books are either too high-level and do not have any benefit for someone with some fundamental understanding of the topic, or they are so technical / mathematical that you would need to be an expert on the topic. The author strikes a very good balance, providing more technical background for readers with a basic grasp of the subject, and offering potentially new perspectives for readers with a higher level of technical expertise. As a plus, the author’s tongue-in-cheek style of writing makes it an enjoyable read.

Stefanie Tatzreiter

Detaillierte und verständliche Erklärung

Reviewed in Germany on February 27, 2020

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Sehr gute praktische Beispiele sowie sind die mathematischen Formeln sehr verständlich erklärt.

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Practical Recommender Systems 1st Edition, Kindle Edition

PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS

PART 2 - RECOMMENDER ALGORITHMS

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About the author

Kim Falk

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