Yahoo! Inc.

H Scott Roy

Deepak Agarwal

Bee-Chung Chen

Wei Chu

Nitin Motgi

Pradheep K Elango

Raghunath Ramakrishnan

Esteban Kozak

Timothy M Nufire

The present invention is directed towards systems and methods for the classification or scoring of content items. The method according to one embodiment comprises providing at least one labeled content item as input to an initial classification model, a given labeled item having at least one feature, a given feature being associated with a weight, computing a posterior probability of the initial classification model for the given labeled content item and generating an updated classification model using the initial classification model and the weight associated with the given feature in the given labeled content item. The updated classification model is applied to an unlabeled content item to determine a score for the unlabeled content item.

System and method for labeling a content item based on a posterior probability distribution

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

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.

Providing a search result in some embodiments comprises receiving a user query, determining whether an equivalent query of the user query is included in a pre-existing query set wherein the pre-existing query set is associated with an answer, and providing the answer. Providing a search result in some embodiments comprises receiving a user query, determining that an equivalent query of the user query is not included in any of a plurality of existing query sets, and adding the user query to one of the plurality of pre-existing query sets.

Query based search engine

A method of providing relevant search information comprises receiving a search term. The method also comprises locating a first search result that matches the search term, wherein the first search result is associated with a first measure of reputation, locating a second search result that matches the search term, wherein the second search result is associated with a second measure of reputation, and preferentially presenting the first and the second search results based on the first and the second measures of reputation.

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Palo Alto, CA, United States of America

H Scott Roy