| 000 | 00000nam u2200205 a 4500 | |
| 001 | 000045977985 | |
| 005 | 20190401154642 | |
| 008 | 190329s2019 nyua b 001 0 eng d | |
| 020 | ▼a 9781617292705 (pbk.) | |
| 020 | ▼a 1617292702 (pbk.) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 082 | 0 4 | ▼a 006.33 ▼2 23 |
| 084 | ▼a 006.33 ▼2 DDCK | |
| 090 | ▼a 006.33 ▼b F191p | |
| 100 | 1 | ▼a Falk, Kim. |
| 245 | 1 0 | ▼a Practical recommender systems / ▼c Kim Falk. |
| 260 | ▼a Shelter Island, NY : ▼b Manning, ▼c c2019. | |
| 300 | ▼a xxiv, 406 p. : ▼b ill. ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Recommender systems (Information filtering). |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.33 F191p | 등록번호 111807146 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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
정보제공 :
목차
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