The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Oct. 15, 2013
Filed:
Aug. 08, 2012
H. Scott Roy, Palo Alto, CA (US);
Raghunath Ramakrishnan, Madison, WI (US);
Pradheep Elango, Mountain View, CA (US);
Nitin Motgi, Santa Clara, CA (US);
Deepak K. Agarwal, Sunnyvale, CA (US);
Wei Chu, Sunnyvale, CA (US);
Bee-chung Chen, Mountain View, CA (US);
H. Scott Roy, Palo Alto, CA (US);
Raghunath Ramakrishnan, Madison, WI (US);
Pradheep Elango, Mountain View, CA (US);
Nitin Motgi, Santa Clara, CA (US);
Deepak K. Agarwal, Sunnyvale, CA (US);
Wei Chu, Sunnyvale, CA (US);
Bee-Chung Chen, Mountain View, CA (US);
Yahoo! Inc., Sunnyvale, CA (US);
Abstract
Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.