The present disclosure is directed to systems and methods for programmatic digital advertisements that are personalized and uniquely targeted to individually-identified consumers via non-personal, but individually accessed devices. The consumer accessing a non-personal device is identified and data cookie pertaining to the user is created and sent to third-party programmatic advertising exchanges for the use in real time bidding, private marketplace deals, or programmatic guaranteed sales. This allows for personalized digital advertisements to be delivered to a specific user accessing a non-personal device.

An electronic marketplace provides a communication platform between consumer and sellers. Consumers create product wish lists and the wish lists are used as the basis for product advertising from the sellers. In addition, a consumer may invite friends to view the wish list and purchase particular products that are added to the wish list. Because wish lists are used as the basis for advertising, consumers can manage the types of product advertising that they see. Consumers have the ability to turn off individual product advertisements by removing that product from the wish list. Sellers utilize reverse bidding to compete for advertising space with each consumer and friend. One particularly advantageous feature of the embodiment is that wish lists serve as nameless and untraceable proxies for each member by keeping personal information removed from the sellers.

An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

Systems and methods for providing real-time searching of audio streams to facilitate content moderation and advertising offer generation are provided. The method includes receiving a plurality of audio streams, converting each of the audio streams, in real-time, into one or more text segments, saving each text segment to a data store of real-time content, receiving one or more advertiser bids, wherein each of the one or more advertiser bids includes one or more bid criteria, determining whether the one or more bid criteria are met for an audio stream of the plurality of audio streams, selecting one or more winning bids from advertiser bids in which the one or more bid criteria have been met, generating one or more advertising offers for each of the one or more winning bids, and presenting the one or more advertising offers to a representative of the audio stream.

An adaptive bidding system for networked advertising. A bid request is received from an exchange over a network. Each bid request represents the opportunity to bid on an advertising opportunity. A processing time limit is determined based at least in part on a network latency measurement and an exchange reported timeout. The execution of one or more tasks is initiated by the adaptive bidding system. A response is determined from one or more results which are available before the processing time limit expires. The execution of any tasks that have not completed execution before the processing time limit expires are terminated. The response is provided to the exchange.

A system and method for digital retail marketing and couponing in a physical retail environment that includes collecting image data across a retail environment; tracking a user within the retail environment; processing the image data and detecting a customer-item interaction event in association with the user; and based in part on the customer-item interaction event, delivering content to an output interface.

Embodiments of the present invention provide for machine learning-based systems and methods for preventing fraud in programmatic advertising. The systems and methods provide for applying a plurality of machine learning models to data associated with a bid request, determining if the bid request is associated with fraudulent activity as a result of the machine learning models, and selectively preventing the bid request from being provided to potential buyers based on the determination.

The present disclosure is directed to methods for identifying a user by a demand side platform (DSP) across advertiser exchanges. The method includes establishing, by a DSP, a cookie mapping for a user. The cookie mapping includes a mapping of user identifiers for the user from advertisement exchanges to a user identifier assigned by the DSP for the user. The DSP stores to the cookie mapping a first mapping to the user identifier of the DSP, comprising a first user id received by a bidder from a first exchange and a first exchange id for the first exchange. A bidder inserts a pixel into a bid for an impression opportunity to a second exchange. The pixel includes a key to the cookie mapping and a second user id for the user and a second exchange id. The second user id is received by the bidder from a second exchange.

Systems, methods, and computer program products are provided for auctioning advertising inventory for various applications, including for mobile applications. In various embodiments, a request for an advertisement is received at a first server from a client device. The request is sent, from the first server, to two or more remote servers. A bid value and an asset to be presented at the client device is received from each of the two or more remote servers. A highest bid is determined from the received bid values. Whether the asset associated with the highest bid is compliant based on the request is determined. When the asset is determined to be compliant, the asset is sent to the client device.

A system and method for digital retail marketing and couponing in a physical retail environment that includes collecting image data across a retail environment; tracking a user within the retail environment; processing the image data and detecting a customer-item interaction event in association with the user; and based in part on the customer-item interaction event, delivering content to an output interface.