Statistical Methods for Recommender Systems - Bee-Chung Chen,Deepak K. Agarwal
-30% ar kodu BOOKS
Piegāde 17-23 darba dienu laikā
30 dienu atgriešanas politika
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 ... Pilns apraksts
Jums varētu patikt arī
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
AI Engineering: Building Applications with Foundation Models
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Art of Game Design: A Book of Lenses
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How To Think About AI: A Guide For The Perplexed
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
HBR Guide to Generative AI for Managers
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Web Application Hacker's Handbook: Discovering and Exploiting Security Flaws
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How Linux Works: What Every Superuser Should Know
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Artificial Intelligence: A Modern Approach, Global Edition
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
C Programming Language
Brian W. Kernighan, Brian Kernighan, Dennis Ritchie
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Proofs from THE BOOK
Martin Aigner, Günter M. Ziegler
-30% ar kodu BOOKS
Piegādātāja noliktavā
Aprašymas
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.
Vairāk informācijas
| Autors | Bee-Chung Chen, Deepak K. Agarwal |
|---|---|
| Izdevējs | Cambridge University Press |
| Izlaides gads | 2016 |
| Vāka tips | Cietais vāks |
| EAN | 9781107036079 |