Predicting User Interactions With Objects Associated With Advertisements On An Online System
作者： Eitan Shay , Richard Bill Sim , Jun Yang , Stuart Michael Bowers
DOI:
关键词:
摘要: Based on prior interactions associated with a user, an online system predicts amount of interaction by the user object advertisement. Using predicted interaction, determines expected value presenting advertisement to user. The is ranked among other advertisements based values advertisements, and one or more are selected for presentation ranking. An may also specify threshold as targeting criteria, so determine if eligible be presented
lens.org 参考文章(38)
Michiel Nolet, David M. Himrod, John H. Dalto, Steven K. Kannan, Targeted learning in online advertising auction exchanges ,(2012)
Andrew S. Dilling, Timothy B. Dixon, Kevin M. Cabral, Systems and Methods for Serving Secure Content ,(2012)
Xuerui Wang, Ruofei (Bruce) Zhang, Order-independent approximation for order-dependent logic in display advertising ,(2011)
Roshan B. Bangera, Zephrin I. Lasker, Geoffrey B. Grauer, Nicholas P. Herman, Brian C. Long, Advertising Using Mobile Devices ,(2012)
Ryan Allen Stout, Alon Michael Shalita, Ming Hua, Dynamic expressions for representing features in an online system ,(2012)
Scott Shapiro, Richard Sim, Meg Griffing Sloan, Defining and Verifying the Accuracy of Explicit Target Clusters in a Social Networking System ,(2010)
Nuwan Sanaratna, Giridhar Rajaram, Targeting users of a social networking system based on interest intensity ,(2011)
David Edward Braginsky, Erick Tseng, Providing relevant notifications for a user based on location and social information ,(2011)
Nitin Motgi, Andrei Broder, Scott Roy, Raghu Ramakrishnan, Bee-Chung Chen, Pradheep Elango, Todd Beaupre, John Tomlin, Deepak Agarwal, Jayavel Shanmugassundaram, Belle Tseng, Presentation of Content Based on Utility ,(2011)
Weiguo Liu, Jinlin Wang, Budget-influenced ranking and pricing in sponsored search ,(2009)