Yahoo! Inc.

Raghunath Ramakrishnan

Deepak Agarwal

Brian Frank Cooper

Bee-Chung Chen

Adam Silberstein

Wei Chu

Nitin Motgi

Pradheep K Elango

H Scott Roy

Jeffrey Terrace

Adam Eli Silberstein

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.

Enhanced matching through explore/exploit schemes

In one embodiment, for each one of one or more content producers, access a content-producing rate at which the content producer produces one or more items of content; and for each one of one or more content consumers, access a content-consuming rate at which the content consumer consumes one or more items of content. For each one of a plurality of consumer-producer pairs, wherein the consumer-producer pair comprises one of the content consumers and one of the content producers, and the content consumer follows the content producer, select between a push strategy and a pull strategy for delivering one or more items of content from the content producer to the content consumer based on the content-consuming rate of the content consumer and the content-producing rate of the content producer.

Mechanism for supporting user content feeds

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.

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Madison, WI, United States of America

Raghunath Ramakrishnan