Yahoo Ad Tech LLC

Verizon Media Inc.

Kavind Aggarwal

Gaurav Chaula

One or more computing devices, systems, and/or methods for determining whether requests for content are fraudulent are provided. A request for content may be received from a first device. A first user profile associated with the first device may be identified. The first user profile may comprise activity information associated with the first device, demographic information associated with the first device and/or interest information associated with the first device. A user profile database may be analyzed to identify a set of user profiles similar to the first user profile. A relevance score associated with the request for content may be generated based upon the resource, the set of user profiles and/or the first user profile. The relevance score may be compared with a threshold relevance to determine whether the request for content is fraudulent.

Identifying fraudulent requests for content

Disclosed are methods and devices for converting mobile notifications to content using a local content exchange. In one embodiment, the method comprises receiving a request for content from a mobile application; querying a data store of available content, the data store of available content populated with one or more notifications received by other mobile applications installed on the mobile device; identifying a responsive notification for the request for content based on the querying, the content item identified based on bid parameters associated with the one or more notifications; and transmitting the responsive notification to the mobile application.

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Delhi, India

Kavind Aggarwal