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《Practical Recommender Systems》高清 PDF 免费下载地址

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    发表于 2019-4-5 15:29:34 | 显示全部楼层 |阅读模式
    本帖最后由 zhanglianhai 于 2019-4-5 15:31 编辑

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    百度网盘下载地址链接(百度云):链接:https://pan.baidu.com/s/1bnohEtLSK5v3biTg-3yWtA 提取码:lkmk
    基本信息
    Practical Recommender Systems
    作者 Kim Falk;
    ISBN13: 9781617292705
    类型 平装(简装书)
    语种: 英语(English)
    出版日期: 2017-8-31
    出版社: Manning Publications
    页数: 432
    重量(克): 725
    尺寸: 23.368 x 18.796 x 2.286 cm

    内容简介
    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

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