Abstract: A computing device provides enhancement of the capabilities of text messaging applications that execute on user devices. The computing device includes a processor that receives a text message via a network from a user device, the text message including a character representation of a product bundle of a merchant. The processor determines bundle data for the product bundle by referencing a database using the character representation. The processor provides one or more text messages via the network to the user device with transaction data including the bundle data, payment data, and shipping data. The processor receives a text message via the network from the user device confirming the transaction data. The user may use text commands presented by the computing device or natural language conversation in the text messages to the computing device to modify the transaction data and perform other ecommerce actions.

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

Abstract: Systems, methods, and computer-readable media are disclosed for dynamically determining relative product performance using quantitative values.

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

Abstract: This disclosure describes systems, methods, and computer-readable media related to online advertisement campaign recommendations. A server may obtain data from an events datastore indicative of missing time slices from a partition of events data. The server may obtain first events data for the missing time slices. The server may identify redundant events data in the first events data that is redundant to second events data stored in the events datastore. The server may delete the redundant events data from the first events data to generate a modified first events data. The server may transmit the modified first events data to the events datastore.

Abstract: Systems and methods are disclosed for providing composite content, such as video content, by compositing. A template content may be received and may have one or more color blocks. One or more drop-in content sets may each have one or more drop-in content that correspond to the one or more colors of the template content color blocks. The one or more color blocks of the template content may be replaced with its corresponding drop-in content from one of the one or more drop-in content sets to generate a composite content. Furthermore, the composite content may have demographic, geographic, and/or behavioral parameters associated with it to enable targeting the composite content, such as in the form of advertisements and/or product or service recommendations, to one or more users.

Abstract: This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.

Abstract: This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.

Abstract: This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.