The rise of internet-based services and products in the late 1990s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven …
Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate …
CA Gomez-Uribe, N Hunt - ACM Transactions on Management …, 2015 - dl.acm.org
This article discusses the various algorithms that make up the Netflix recommender system, and describes its business purpose. We also describe the role of search and related …
R Kohavi, R Longbotham - … of machine learning and data mining, 2015 - exp-platform.com
Many good resources are available with motivation and explanations about online controlled experiments (Kohavi et al. 2009a, 2020; Thomke 2020; Luca and Bazerman …
D Jannach, M Jugovac - ACM Transactions on Management Information …, 2019 - dl.acm.org
Recommender Systems are nowadays successfully used by all major web sites—from e- commerce to social media—to filter content and make suggestions in a personalized way …
RA Lewis, JM Rao - The Quarterly Journal of Economics, 2015 - academic.oup.com
Twenty-five large field experiments with major US retailers and brokerages, most reaching millions of customers and collectively representing $2.8 million in digital advertising …
S Gupta, R Kohavi, D Tang, Y Xu, R Andersen… - ACM SIGKDD …, 2019 - dl.acm.org
Online controlled experiments (OCEs), also known as A/B tests, have become ubiquitous in evaluating the impact of changes made to software products and services. While the concept …
Online evaluation is one of the most common approaches to measure the effectiveness of an information retrieval system. It involves fielding the information retrieval system to real users …