Yahoo! Inc.

Erick Cantu-Paz

Kannan Achan

Rukmini Iyer

Christopher C LuVogt

Eren Manavoglu

Christopher John Leggetter

Ozgur Cetin

Jan O Pedersen

Su-Lin Wu

Ashvin Kannan

Chad Carson

Pero Subasic

David Cho-Lun Ku

Divy Kothiwal

The present invention is directed towards systems and methods for providing dynamic search results based upon historical data through the use of one or more widgets. The method of the present invention comprises receiving a request for content from a client and generating one or more widgets for providing search result content. A display profile is applied to the one or more widgets and the one or more widgets are combined with static search results to form a search result page that is provided to a requesting client.

Dynamic layout for a search engine results page based on implicit user feedback

A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.

Using linear and log-linear model combinations for estimating probabilities of events

A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.

Using clicked slate driven click-through rate estimates in sponsored search

The subject matter disclosed herein relates to maintaining a history of user interaction data within a sliding window, where the sliding window may be sized based at least in part on a quantification of such user interaction.

Adaptive user feedback window

Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate ('CTR') that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

Click probability with missing features in sponsored search

A method of facilitating the endorsement of products through Internet advertisements accepts a bid for an endorsement, enables communication, associated with the bid, between an advertiser and a potential endorser, and serves an endorsement associated with the bid. In one implementation, the endorsement is displayed together with a symbol verifying the endorser.

System and method for internet marketing by endorsements

Dynamic layout for a search engine results page on implicit user feedback

The present invention is directed towards systems and methods for predicting a frequency with which an advertisement displayed in response to a query will be selected. The method of the present invention comprises receiving analytics data associated with a display of one or more advertisements in response to one or more queries. One or more features associated with the one or more advertisements displayed in response to the one or more queries are identified. One or more functions are generated for predicting a frequency with which a given advertisement displayed in response to a query will be selected using the analytics data and features associated with the one or more advertisements displayed in response to the one or more queries.

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Sunnyvale, CA, United States of America

Erick Cantu-Paz