Statistical Methods for Recommender Systems - Bee-Chung Chen,Deepak K. Agarwal
-30% koodiga BOOKS
Saadetis 17-23 tööpäeva jooksul
30-päevane tagastamisõigus
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 ... Täielik kirjeldus
Võib-olla meeldib sulle ka
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-30% koodiga BOOKS
Meie tarnija laos
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-30% koodiga BOOKS
Meie tarnija laos
TOP
AI Engineering: Building Applications with Foundation Models
-30% koodiga BOOKS
Meie tarnija laos
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-30% koodiga BOOKS
Meie tarnija laos
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-30% koodiga BOOKS
Meie tarnija laos
TOP
The Art of Game Design: A Book of Lenses
-30% koodiga BOOKS
Meie tarnija laos
TOP
How To Think About AI: A Guide For The Perplexed
-30% koodiga BOOKS
Meie tarnija laos
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-30% koodiga BOOKS
Meie tarnija laos
TOP
HBR Guide to Generative AI for Managers
-30% koodiga BOOKS
Meie tarnija laos
TOP
The Web Application Hacker's Handbook: Discovering and Exploiting Security Flaws
-30% koodiga BOOKS
Meie tarnija laos
TOP
How Linux Works: What Every Superuser Should Know
-30% koodiga BOOKS
Meie tarnija laos
TOP
Artificial Intelligence: A Modern Approach, Global Edition
-30% koodiga BOOKS
Meie tarnija laos
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-30% koodiga BOOKS
Meie tarnija laos
TOP
CompTIA Security+ Certification Kit: Exam SY0-701
-30% koodiga BOOKS
Meie tarnija laos
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-30% koodiga BOOKS
Meie tarnija laos
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-30% koodiga BOOKS
Meie tarnija laos
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-30% koodiga BOOKS
Meie tarnija laos
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-30% koodiga BOOKS
Meie tarnija laos
TOP
Proofs from THE BOOK
Martin Aigner, Günter M. Ziegler
-30% koodiga BOOKS
Meie tarnija laos
Kirjeldus
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.
Lisateave
| Autor | Bee-Chung Chen, Deepak K. Agarwal |
|---|---|
| Kirjastaja | Cambridge University Press |
| Väljalaskeaasta | 2016 |
| Kaanetüüp | Kõvakaaneline |
| EAN | 9781107036079 |