Snap Inc.

Other

David B Lue

Jason Brewer

Rodrigo B Farnham

Nicholas J Stucky-Mack

Nima Khajehnouri

Zhuo Xu

Chinmay Lonkar

John Cain Blackwood

Kevin Lee Penner

Roman Grachev

Weizhi Li

Yuqi Deng

Vineet Abhishek

Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for predicting a conversion rate. The program and method provide for receiving, from an advertisement service, a bid to display a first advertisement at a computing device; determining, in response to receiving the bid, a set of features that relate to the first advertisement; providing the set of features to a machine learning model configured to output a predicted conversion rate for the first advertisement, the machine learning model having been trained based on multi-task learning using plural sets of features corresponding to plural second advertisements, the plural sets of features being associated with both click-through conversions and view-through conversions; and determining, based on the output of the machine learning model with respect to the set of features, the predicted conversion rate for the first advertisement.

Predicting a conversion rate

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for selecting ads for a video. The program and method provide for receiving a request for an ad to insert into a video playing on a client device, the request including a first content identifier that identifies a first type of content included in the video; determining a set of content identifiers associated with the first content identifier, the set of content identifiers identifying second types of content to filter with respect to providing the ad in response to the request; selecting an ad from among plural ads, by filtering ads tagged with a second content identifier included in the set of content identifiers; and providing the selected ad as a response to the request.

Selecting ads for a video within a messaging system

Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Systems, devices, and methods for content selection

A content request is received from a device of a user. A plurality of candidate content items is identified. Each candidate content item has a bid value. A relevancy value is automatically generated for each candidate content item. The relevancy value indicates whether the candidate content item is likely to be skipped by the user. For each candidate content item, a combined value is automatically generated by adjusting the bid value using the relevancy value generated for the candidate content item. One or more candidate content items are automatically selected based on the combined value generated for each of the one or more candidate content items. The one or more selected candidate content items are automatically integrated into at least one placeholder area among one or more pre-selected content items as part of the aggregated content. The aggregated content is presented on the device of the user.

Real-time content integration based on machine learned selections

A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.

A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

A dental chair armrest having a stationary bar assembly, a movable bar assembly, and a supporting assembly. The stationary bar assembly is removably mounted onto a dental chair. The stationary bar assembly has a stationary bar, a plate, and a stationary bar nut. The stationary bar has first and second studs, and a threaded stud. The movable bar assembly has a bar and a movable bar nut. The bar has a hole and a bar stud that perpendicularly extends from the bar. The supporting assembly has an adjustable bar, a platform, a swivel, and a support pad. The adjustable bar comprises at least two holes. The platform is connected to the adjustable bar with a swivel, and the support pad is mounted onto the platform. The stationary bar assembly is connected to the movable bar assembly, and the movable bar assembly is connected to the supporting assembly.

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Santa Monica, CA, United States of America

David B Lue