An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system generates the feed of content including organic content items and sends the feed of content to a client device while selecting sponsored content items for the feed of content. The online system transmits selected sponsored content items to the client device, which modifies the feed of content to include the sponsored content items and presents the modified feed of content.

A system for targeting advertising comprises a processor configured to compute a similarity score between a plurality of customers in a seed set and each of a pool of in-store purchasers having no history of online purchases from the online store of an entity. Each user in the seed set has a history of online purchases from the online store of the entity and a history of in-store purchases from stores of the entity. The processor is configured for selecting a subset of the pool of in-store purchasers having similarity scores above a predetermined threshold. The processor is configured for initiating rendering of advertisements for the online store to the subset of the pool of in-store purchasers.

The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.

An electronic system may be configured to determine if a first-type device ID and a second-type device ID both identify a same device. To do so, the electronic system may preliminarily group a particular first-type ID with a particular second-type ID based on matching time slots and network addresses. Individual scores for each of the preliminary groups may be determined based on cardinalities associated with the time slots. Combined scores may then be determined for groups having the same first-type device ID and second-type device ID. A final analysis may be performed to confirm, with a sufficient level of confidence, whether first-type device IDs and second-type device IDs included in the preliminary groups identify the same device.

A system includes a computing device including a processor programmed to receive data identifying a mental state of a user, the data including at least one of a user physical condition and a user communication. Based on the mental state data, the processor is programmed to assign one or more stored keywords to the user, and provide media content to the user based on the keywords assigned to the user based on the mental state data.

An electronic device and method for replacing and outputting an advertisement are provided. The electronic device includes: a memory storing at least one program; a communication unit configured to receive context data to be used to determine a state of a user, from at least one external device; and a processor configured to replace and output an advertisement by executing the at least one program, wherein the at least one program includes instructions to: acquire user state information indicating the state of the user from the received context data, based on a learning model using one or more neural networks; and perform control to replace a previously determined first advertisement with a second advertisement determined based on the user state information and to output the second advertisement.

Methods and systems and mobile device interfaces for creating, joining, organizing and managing via mobile devices affinity groups in a cloud computing environment for social and business purposes.

A merchant point-of-sale (“POS”) system architecture is provided. The POS system architecture may include a POS terminal including a POS receiver, a virtual display and a transmitter, and a transaction processing network including a network receiver and processor. The network receiver may be configured to receive a payment authorization request. The virtual display may be configured to display selectable options. The options may be displayed prior to processing the payment authorization request. The selectable options may include a first and second option. The first option may be to apply a merchant-funded rewards program network to the payment authorization request. The second option may be to continue to apply the default issuer funded rewards program network to the payment authorization request. When the first option is selected, a processor may be configured to terminate the issuer funded reward program network and activate the merchant-funded rewards program network.

The present disclosure identifies and/or delivers tailored content based upon a service dialog. For example, the systems may receive a request for tailored content, facilitate a service dialog to obtain information related to the request, and communicate a plurality of tailored content based upon the information related to the request. Further, the systems may identify tailored content based upon a consumer profile, communicate the tailored content to a web client, and/or receive a selection of the tailored content. Further still, the systems may modify a magazine (e.g., content that is presented electronically) based upon tailored content.

Machine learning models can be trained to vend profiles with a high likelihood of matching with a user comprises: training machine learning models with feature vectors representing potential profile matches identified as input to a user match selection process, updating the trained models with feature vectors identified as further input to the selection process, in response to the updating, swapping the trained model out of and a further trained machine learning model into a foreground execution space, resulting in the untrained model being in the foreground and the trained model being in a processing background, after satisfying a defined criterion, injecting an outlier potential match entity as supplemental input to the further training with a supplemental feature vector as further input to the selection process, determining respective confidence values corresponding to candidate profiles accessible to the selection process, and rendering profile images of candidate profiles for the selection process.