Practical Recommender Systems

432 Pages·2019·14.771 MB·English

by  Kim Falk

Kim Falk
M A N N I N G
Practical Recommender Systems

Practical
Recommender
Systems
KIM FALK
MANNING
SHELTER ISLAND
For online information and ordering of this and other Manning books, please visit
www.manning.com. The publisher offers discounts on this book when ordered in quantity.
For more information, please contact
Special Sales Department
Manning Publications Co.
20 Baldwin Road
PO Box 761
Shelter Island, NY 11964
Email: [email protected]
© 2019 by Manning Publications Co. All rights reserved.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in
any form or by means electronic, mechanical, photocopying, or otherwise, without prior written
permission of the publisher.
Many of the designations used by manufacturers and sellers to distinguish their products are
claimed as trademarks. Where those designations appear in the book, and Manning
Publications was aware of a trademark claim, the designations have been printed in initial caps
or all caps.
Recognizing the importance of preserving what has been written, it is Manning’s policy to have
the books we publish printed on acid-free paper, and we exert our best efforts to that end.
Recognizing also our responsibility to conserve the resources of our planet, Manning books
are printed on paper that is at least 15 percent recycled and processed without the use of
elemental chlorine.
Manning Publications Co. Development editor: Helen Stergius
20 Baldwin Road Production editor: Janet Vail
PO Box 761 Copy editors: Katie Petito and Frances Buran
Shelter Island, NY 11964 Proofreader: Elizabeth Martin
Technical proofreaders: Valentin Crettaz and Furkan Kamaci
Typesetter: Dottie Marsico
Cover designer: Marija Tudor
ISBN 9781617292705
Printed in the United States of America
1 2 3 4 5 6 7 8 9 10 – SP – 24 23 22 21 20 19
To the loves of my life:
my wife, Sara, and my son, Peter,
the small Superhero

brief contents
PART 1 GETTING READY FOR RECOMMENDER SYSTEMS...................1
1 ■ What is a recommender? 3
2 ■ User behavior and how to collect it 30
3 ■ Monitoring the system 57
4 ■ Ratings and how to calculate them 77
5 ■ Non-personalized recommendations 102
6 ■ The user (and content) who came in from the cold 128
PART 2 RECOMMENDER ALGORITHMS........................................149
7 ■ Finding similarities among users and among content 151
8 ■ Collaborative filtering in the neighborhood 181
9 ■ Evaluating and testing your recommender 211
10 ■ Content-based filtering 248
11 ■ Finding hidden genres with matrix factorization 284
12 ■ Taking the best of all algorithms: implementing hybrid
recommenders 329
13 ■ Ranking and learning to rank 357
14 ■ Future of recommender systems 384
vii

contents
preface xvii
acknowledgments xix
about this book xx
about the author xxiii
about the cover illustration xxiv
PART 1 GETTING READY FOR RECOMMENDER SYSTEMS....1
1 What is a recommender? 3
1.1 Real-life recommendations 3
Recommender systems are at home on the internet 5 ■ The long
tail 5 ■ The Netflix recommender system 6 ■ Recommender
system definition 12
1.2 Taxonomy of recommender systems 15
Domain 15 ■ Purpose 16 ■ Context 16 ■ Personalization
level 17 ■ Whose opinions 18 ■ Privacy and
trustworthiness 18 ■ Interface 19 ■ Algorithms 22
1.3 Machine learning and the Netflix Prize 23
1.4 The MovieGEEKs website 24
Design and specification 26 ■ Architecture 26
1.5 Building a recommender system 28
ix