Aol Inc.

Yahoo Ad Tech LLC

Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Aol Advertising Inc.

Oath (americas) Inc.

Thu R Kyaw

Sang Chul Song

Saad Mufti

Sudhir Achuthan

Sean Christopher Timm

Vineet Mahajan

Eric Bosco

Matthew Nguyen

Qiuming Yan

John Franklin Pavley

Jonathan Ji

Elena Haliczer

Chin Foh Hew

Mikhail Nicholaos Barrett

Aaron T Soules

Systems and methods are disclosed for online distribution of content based on a user sentiment index. The method may include receiving, over a network and from a user device, one or more user generated inputs and calculating the user sentiment index based on the one or more user generated inputs. The method may also include receiving, over the network, from a content or advertising provider, instructions on publishing content or advertising to a webpage based on the calculated user sentiment index, and publishing content for display on user devices over the network based on a comparison of the calculated user sentiment index and the received instructions.

Systems and methods for providing and using an internet sentiment index

Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation.

Systems and method for performing contextual classification using supervised and unsupervised training

Methods, systems, and computer-readable media are disclosed for dynamic partitioning in distributed computing environments. One method includes: receiving a first data set and a second data set; mapping the first data set into a first set of key-value pairs; mapping the second data set into a second set of key-value pairs; estimating, using a sketch, a frequency count for each key based on the first set of key-value pairs and the second set of key-value pairs; determining whether the estimated frequency count for each key is greater than or equal to a predetermined threshold; and partitioning the key when the estimated frequency count for the key is greater than or equal to the predetermined threshold.

Systems and methods for dynamic partitioning in distributed environments

According to one aspect of the present disclosure, a method is provided for advertising on a content-screened web page. The method includes receiving an impression request for a URL for which an advertising impression is desired; screening HTML content of a web page identified by the URL; generating a rating for the URL based on the HTML content of the web page; assigning the rating to the impression request; and serving an advertising impression on the web page based on the rating.

Systems and methods for advertising on content-screened web pages

Computer-implemented systems and methods are provided for presenting content recommendations. In one implementation, a method is provided for providing content recommendations to a plurality of users. The method may include generating a first plurality of content recommendations for a first user using a first plurality of methods, each of the first plurality of methods having a weight associated therewith. The method may further include selecting a second plurality of content recommendations from the first plurality of content recommendations based on the weights associated with the first plurality of methods. The method may further include providing the second plurality of content recommendations to the first user. The method may further include updating the weights associated with the first plurality of methods based on engagement by the first user with the second plurality of content recommendations.

Systems and methods for providing personalized recommendations for electronic content

Systems and methods are provided for processing and displaying user-generated content. User-generated content is received from a user over a network. A conversation related to the received user-generated content is identified. A score associated with the identified conversation is calculated based on one or more parameters related to the user or the conversation. The user-generated content is provided for display in relation to the conversation, based on the calculated score associated with the conversation.

Systems and methods for processing and displaying user-generated content

Techniques are provided for controlling access to an application. A first request to use an application may be received from a first user and an application access rule associated with the first user may be accessed. Based on the application access rule, it may be determined that permission is required for the first user to use the application. In response, a second request may be sent to a second user requesting permission for the first user to use the application and a response to the second request may be received from the second user. The response may include authorization information used in determining whether the first user has permission from the second user to use the application. In response to receiving the response from the second user, the first request to use the application initiated by the first user may be handled based on the authorization information.

Application unlock

Systems and methods for performing contextual classification using supervised and unsupervised training

Systems and methods are provided for advertising on content-screened web pages. In one implementation, a method is provided that includes receiving an impression request for which an advertising impression is desired, screening HTML content of a web page identified by the impression request, generating a rating based on the HTML content of the web page, and assigning the rating to the impression request.

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Reston, VA, United States of America

Thu R Kyaw