A profiling system including one or more processing devices assigns a classification to a particular user based on anonymous on-line user activities engaged in by the particular user via a plurality of different on-line sites. The profiling system determines that the particular user is currently accessing a particular on-line site. In response to determining that the particular user is currently accessing the particular on-line site, the profiling system delivers selected content to the particular user, wherein the selected content is automatically selected based on the classification.

Methods, apparatus, systems, and articles of manufacture for communication with an audience metering device are disclosed. An example apparatus includes one or more non-transitory computer readable media, instructions in the apparatus, and one or more processors to execute the instructions. The example one or more processors are to segment a message to be transmitted to a configuration device into a first message segment and a second message segment, store the first message segment in a characteristic memory, and transmit a first advertisement to the configuration device when the first message segment is stored in the characteristic memory. Additionally, the example one or more processors are to after the first message segment has been gathered by the configuration device, store the second message segment in the characteristic memory and transmit a second advertisement to the configuration device when the second message segment is stored in the characteristic memory.

Provided is a technique for realizing targeting in which social connections are considered. An information processing apparatus acquires factual features of each of a plurality of users as user features, and based on the user features, creates a relationship graph showing a social relationship between the plurality of users, sets one or more users among the plurality of users as a target user group, and based on the relationship graph and the user features of the target user group, adds one or more users, among the plurality of users, having a social relationship with a user included in the target user group to the target user group and determines the resulting target user group as an expanded user group.

This application discloses a method for training a content recommendation model performed by a computer device. The method includes: obtaining a sample data set; inputting sample data into a probability prediction model to output a probability prediction result; inputting the sample data into a duration prediction model to output a duration prediction result; determining, based on interaction data between a historical account and a historical recommendation content, probability prediction loss corresponding to the probability prediction result and duration prediction loss corresponding to the duration prediction result; and training the probability prediction model based on the probability prediction loss and the duration prediction loss to obtain the content recommendation model, the content recommendation model predicting a recommendation probability of recommending a target content to a target account. The foregoing solution improves the accuracy of a predicted probability of recommending a target content to a target account.

One variation of a method includes: receiving a request for advertising content from an ad slot loaded with a webpage rendered by a computing device; accessing a set of ad slot characteristics including an address of the webpage; extracting a set of visual assets, representing advertising content, from a digital advertisement selected for presentation within the ad slot; selecting a presentation layer, in a set of presentation layers, for pairing with the digital advertisement based on the set of visual assets and the set of ad slot characteristics, the presentation layer defining an interactive format and a layout of visual assets within the interactive format; transforming the digital advertisement into an interactive advertisement according to the presentation layer; and rendering the interactive advertisement within the ad slot responsive to an event that moves the ad slot within a viewing window of the computing device.

Transformation of inputs including beacon inputs, Global Positioning System (GPS) inputs, captured panorama inputs, user-penned descriptive inputs, and payment-amount-specifying inputs, via components, into outputs including user device POS configuration setting outputs and/or payment-gateway-directed authorization request outputs. Further, transformation of inputs including POS scanner inputs, POS keyboard inputs, and/or POS printer-directed inputs, via components, into outputs including compliance check outputs, tagged omnibus record outputs, SKU-UPC mapping outputs, and/or convergence/correlation outputs. Additionally, transformation of inputs including older limited-capability POS software image inputs and/or newer limited-capability POS software image inputs, via components, into outputs including update directive outputs. Functionality set forth includes allowing user devices to directly communicate with payment gateways, capturing and making use of scanner-obtained data and printer-destined data in a way that does not require code alternation of already-installed POS software, and allowing software of limited-capability POS devices to be updated without sending large, full-overwrite software images.

This disclosure describes systems and methods for establishing promotions for sellers and promoting images of items to users on behalf of sellers. A management service receives a source location identifier from a seller, processes images stored in an image data store to determine images that include the source location identifier in the corresponding image information and includes those images in a promotion that is established for the seller. Likewise, the management service may determine users that have previously interacted with the images and include those users in the promotion campaign.

A method is disclosed for enabling a network location to provide an ordering process for data relevant to connected network devices' activities. The method includes assembling the data, utilizing the activity data, and associating the data, such that information is derived to enable a desired expansion of at least one designated activity. Another method is disclosed for managing an object assignment broadcast operations for a network location based on a network device's previous activities. This second method includes tracing a network device's conduct to determine that a network device prefers a particular class of content. The method also includes tagging a network device's profile with the respective observation and deciding by a network location as to the classification factor for a network device to be targeted for an object assignment broadcast.

Transaction modification based on modeled profiles is described. In an example, transaction data can be received by a payment processing system. The payment processing system can generate, using a trained model, profiles. Based at least in part on (i) receiving an indication that a buyer and a merchant are engaged in, or likely to be engaged in, a transaction and (ii) determining that a characteristic of the transaction corresponds to a profile, generating a communication to enable the buyer or the merchant to modify the transaction in association with processing of the transaction.

Protected audience selection system. Media consumption histories of browsers which have converted are received at a modeling system where targeting of browsers is prohibited. A model is built by determining a frequency of each respective media consumption event among the histories and comparing each determined frequency of a respective media consumption event to a frequency of the respective media consumption event among a population of browsers without the conversion event. The model is sent to a targeting system which excludes conversion events. A description of the conversion event is received at the targeting system. A history of a targetable browser is received at the targeting system. The model is applied to the history of the targetable browser at the targeting system, where conversion events have been excluded from the history. Advertising content is sent to the targetable browser according to a result of applying the model.