This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Social BI indicates the process of gathering, analyzing, publishing, and sharing data, reports, and information. Popularity is not just chosen to measure quality, but also to measure business value. Through feedback mechanisms including comments, ratings, tags, blogs, and microblogs, the results of published BI can be enhanced.
Social BI indicates the process of gathering, analyzing, publishing, and sharing data, reports, and information. Popularity is not just chosen to measure quality, but also to measure business value. Through feedback mechanisms including comments, ratings, tags, blogs, and microblogs, the results of published BI can be enhanced.
by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.
This renders measures like classification accuracy meaningless. The use of multiple measurements in taxonomic problems. Proceedings of the Fourth International Conference on KnowledgeDiscovery and Data Mining, 73–79. Morgan Kaufmann Publishers Inc. Machine Learning, 57–78. Dua, D., & Graff, C. Quinlan, J.
Companies like Google [2], Amazon [3], and Microsoft [4] have all published scholarly articles on this topic. For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. Proceedings of the 13th ACM SIGKDD international conference on Knowledgediscovery and data mining.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content