Examples of the present disclosure describe systems and methods for visit prediction using machine learning (ML) attribution techniques. In aspects, data relating to users and their venue visits is collected and merged with data relating to various directed information impressions. Features of the merged data are identified for one or more time intervals and assigned values and/or labels. The identified features and corresponding values/labels may be used to train an ML model to provide a visit probability for each user represented in the merged data. Based on the visit probabilities provided by the ML model, the percentage increase (or lift) in venue visit rates attributable to the directed information impressions can be accurately estimated

Methods and systems for handling online requests based on information known to a service provider. One method may comprise: obtaining first information, the first information relating to an online request made using a communication apparatus; using a logical identifier assigned to the communication apparatus to obtain second information, the second information pertaining to a profile associated with the logical identifier; comparing the first information to the second information; and performing an action related to handling of the online request based on a result of the comparing.

A method and device for processing promotion information and a system are provided. The method includes that: agreement information and exposure requirements of all promotion information within a preset period are acquired (101); directional delivered targets are determined according to the agreement information and the exposure requirements, and the directional delivered targets are split into multiple non-intersected delivered target sets (102); the promotion information is delivered to users corresponding to the corresponding delivered target sets according to the exposure requirements (103); statistics about social propagation amounts of to the delivered promotion information is made in real time in a delivery process (104); and exposure parameters are corrected according to the social propagation amounts (105), so that delivery of the promotion information is regulated in real time. By the method, the effectiveness and accuracy of delivering the promotion information may be improved.

Systems and methods are disclosed for online distribution of content based on a user sentiment index. The method may include receiving, over a network and from a user device, one or more user generated inputs and calculating the user sentiment index based on the one or more user generated inputs. The method may also include receiving, over the network, from a content or advertising provider, instructions on publishing content or advertising to a webpage based on the calculated user sentiment index, and publishing content for display on user devices over the network based on a comparison of the calculated user sentiment index and the received instructions.

The disclosure relates to a computer server system implementing a method to obtain a plurality of online articles for display on a webpage; obtain a candidate promoted content for each of the plurality of online articles; for each of the plurality of online article and the corresponding candidate promoted content pairs: determine a virality score of the online article indicating popularity of the online article among online users; determine a similarity score indicating similarity between the online article and the candidate promoted content; determine a qualification score based on the virality score and the similarity score; select a pair of target article and target promoted content from the plurality of article and candidate promoted content pairs based on the corresponding qualification scores; and display the target promoted content on the webpage.

The present disclosure describes a system, method, and computer program for automatically predicting the emotion(s) to which a user is most likely to respond based on user characteristics and for tailoring a marketing message to a user based on the predicted emotion(s). A statistical model is created that predicts how a user with certain characteristics will respond to certain emotions. User characteristics are the input to the model, and, for each of a set of emotions, the output is the probability of a corresponding user responding to a message with the emotion. The statistical model is used to generate promotional messages that are tailored to each user based on the emotion to which the user is predicted to respond best, given the user's characteristics.

Systems and methods for controlling contacts with a client's users make use of segment-based contact limits. A contact limit sets a maximum number of contacts that a client can have with a user within a predetermined time window. A segment-based contact limit only applies the contact limit to a subset of all the client's users. The type of contact being limited could include messages that are sent to the user or advertising or sales campaigns that are conducted for the user. A segment is a subset of all of the client's users, and a segment may be defined based on one or more filters.

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that, among other things, provide a marketplace platform accessed within various application environments, such as a gaming environment associated with a video game. The market platform automatically determines product advertisements that appropriate for presentation within the gaming environment based on an application of trained machine learning and artificial intelligence models to selected elements of input data, and facilitates a purchase of advertised products without a user's exit from the gaming environment by maintaining centralized elements of profile data characterizing the user.

A method implemented on a dongle device for controlling menu board items on an attached display device includes receiving content for a menu board from an electronic computing device. The menu board content is transferred to the display device for display on the display device. Information is received regarding sales of items listed on the menu board. The information identifies purchasing trends for the items listed on the menu board. A display of the menu board items is dynamically updated based on the identified purchasing trends.

A process is disclosed for obtaining selected item data from a media processor, wherein the selected item data indicates an item used by an actor in a video data stream. The video data stream includes data representing the item used by the actor and icon data including multiple icons associated with a number of mobile devices. Selected icon data is obtained from the media processor, indicating a selected one of the number of mobile devices. Advertising data for the selected item is provided to the selected one of the number of mobile devices via a communication network. The advertising data is sent when the selected one of the number of mobile devices comes in proximity with a merchant having the selected item in stock. A system is disclosed for performing the process. A machine-readable device is also disclosed for use by the system and process.