Skip to content
Cart

Your Cart

×

You have 0 items in your cart.

Register Sign in Wishlist

Statistical Methods for Recommender Systems

£34.99

  • Date Published: February 2016
  • availability: In stock
  • format: Hardback
  • isbn: 9781107036079
Average user rating
(1 review)

£ 34.99
Hardback

Add to cart Add to wishlist

Other available formats:
eBook


Looking for an inspection copy?

This title is not currently available on inspection

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

    • Includes technical solutions together with open source software for four common recommender settings, with special attention to the online aspects
    • Provides a good introduction to 'classical' approaches to recommender problems
    • Features an open-source library for fitting latent factor models
    Read more

    Customer reviews

    03rd May 2017 by Escafons

    Es de gran ayuda para comprender como funcionan los sistemas de recoemndacion

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: February 2016
    • format: Hardback
    • isbn: 9781107036079
    • length: 298 pages
    • dimensions: 235 x 157 x 20 mm
    • weight: 0.54kg
    • contains: 66 b/w illus. 18 tables
    • availability: In stock
  • Table of Contents

    Part I. Introduction:
    1. Introduction
    2. Classical methods
    3. Explore/exploit for recommender problems
    4. Evaluation methods
    Part II. Common Problem Settings:
    5. Problem settings and system architecture
    6. Most-popular recommendation
    7. Personalization through feature-based regression
    8. Personalization through factor models
    Part III. Advanced Topics:
    9. Factorization through latent dirichlet allocation
    10. Context-dependent recommendation
    11. Multi-objective optimization.

  • Authors

    Deepak K. Agarwal, LinkedIn Corporation, California
    Dr Deepak Agarwal is a big data analyst with more than fifteen years of experience developing and deploying state-of-the-art machine learning and statistical methods for improving the relevance of web applications. He is also experienced in conducting new scientific research to solve notoriously difficult big data problems, especially in the areas of recommender systems and computational advertising. He is a Fellow of the American Statistical Association and associate editor of two top-tier journals in statistics.

    Bee-Chung Chen, LinkedIn Corporation, California
    Dr Bee-Chung Chen is a Senior Staff Engineer and Applied Researcher at LinkedIn. He has been a key designer of the recommendation algorithms that power LinkedIn homepage and mobile feeds, Yahoo! homepage, Yahoo! News and other sites. Dr Chen is a leading technologist with extensive industrial and research experience. His research areas include recommender systems, machine learning and big data analytics.

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×