Systems, methods, and computer-readable media (transitory and non-transitory) are provided herein for automated submission for solicited application slots. In various embodiments, a digital component source process executing on a first computing system may determine a device identifier associated with an application slot to be populated with digital component(s). The application slot may be solicited by a digital component liaison process executing on the first computing system or a second computing system. The digital component source process may retrieve application slot attainment parameter(s) associated with the device identifier. The application slot attainment parameter(s) may be generated based on location ordinal(s) associated with the device identifier. The digital component source process may determine, based on the retrieved application slot attainment parameters, a submission to populate the application slot with a particular digital component item. The digital component source process may provide the submission to the digital component liaison process.

A system described herein may use automated techniques, such as machine learning techniques, to identify products that a user may be interested in purchasing. For example, a model may be created for a user, and attributes of products available for sale may be compared to the model. When determining that a user may be interested in a particular product, a graphical user interface (“GUI”) may be pre-populated and presented to a device of the user, to facilitate the user purchasing the product with minimal interaction.

This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.

The present disclosure is directed to associating computing devices with each other based on computer network activity for selection of content items as part of an online content item placement campaign. A first linking factor is identified based on a connection between a first device and the computer network via a first IP address during a first time period, and based on a connection between a second device and the computer network via the first IP address during the first time period. A number of devices that connect with the computer network via the first IP address is determined. A positive match probability is generated. A second and third linking factors are monitored. A negative match probability is determined based on the second and third linking factors. The first device is linked with the second device based on the positive and negative match probabilities.

This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving ad information from a seller; generating, using one or more processors, a single conversion ad using the received ad information, where the single conversion ad has an available inventory of one such that only a single conversion of the ad can be performed; transmitting the single conversion ad to one or more potential buyers; receive an input from one of the one or more potential buyers; and notifying the seller of the user input. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.

The present disclosure is directed toward systems and methods that allow users to efficiently and effectively create and identify segments of usage patterns. For example, systems and methods described herein allow marketers to query and return sequential segments including sequence conditions based on user-defined dimension item values. Furthermore, systems and methods described allow marketers to query and return sequential segments including sequential events based on user-defined dimension variables. In addition to the foregoing, systems and methods described herein allow marketers to query and return sequential segments defined by repeated events performed at given regularity or frequency.

Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

The present invention relates to methods and systems for targeting and retargeting individuals with advertisement spots during television broadcasting. The method and system enable an advertiser for identifying and categorizing a set of viewers or individuals for retargeting advertisement based on parameters such as, but not limited to, interests or preferences of the individuals, past purchases and interactions of the individuals with the advertiser. The method and system further enable the advertiser to segregate the plurality of individuals into subgroups on the basis of information such as, but not limited to, demography, psychographic and behavioral characteristics of the plurality of individuals. The method and system then enable the advertiser to define one or more advertisement spots and corresponding advertisements to be delivered to different sub groups of individuals based on the categorization. Thereafter, the method and system retarget individuals by sending individualized messages in the one or more advertisement spots.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.