文件名称:
Practical Recommender Systems
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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!www.allitebooks.com
Practical
Recommender
Systems
KIM FALK
MANNING
SHELTER ISLAND
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To the loves of my life
wnife, Sara, and my son, Peter,
the small superhero
brief contents
PART1 GETTING READY FOR RECOMMENDER SYSTEMS
1 What is a recommender 3
User behavior and how to collect it 30
3 Monitoring the system 57
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
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
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
What is a recommender? 3
1.1 Real-life recommendations 3
tail 5. The Netflix recommender system 6. Recommender
Recommender systems are at home on the internet 5. The long
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
3 Machine learning and the netflix Prize 23
1. 4 The Movie geeks website 24
Design and specification 26. Architecture 26
1.5 Building a recommender system 28
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