Systems and methods are provided for calculating a savings amount for loan refinancing. According to one implementation, a method includes displaying a graphic user interface comprising a widget at a client device and receiving input data from a user regarding a loan. In some embodiments, the input data may include the user's credit level and the current monthly payment, APR, and payoff amount associated with the user's current loan. The method further includes determining a saving amount based on a comparison of the input data with historical savings data accessible to the widget, and displaying the savings amount in the graphic user interface.

A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to generate a merchant score for a plurality of merchants based at least in part on merchant statistics and display a user interface on a user device of a user. The user interface includes a subset of the merchants that have been identified for a user based at least in part on the merchant score. Clickstream data can be received from the user interface displayed on the user device. An updated merchant score can be generated based at least in part on the user preference. A sorting habit for the user is determined based at least in part on a user-specified sort received from the user interface. A modified subset of the merchants is displayed on the user device based on the sorting habit.

Provided is an advertisement delivery system including an acquisition unit configured to acquire location information regarding a first user, a delivery unit configured to enable, on the basis of location information regarding an advertisement providing shop that has been registered and the location information regarding the first user, delivery of predetermined content with advertisement information from the advertisement providing shop, and a setting unit configured to set an experienceable period for the first user that indicates a period in which the first user is allowed to experience the predetermined content, in which the setting unit updates the experienceable period for the first user on the basis of the location information regarding the advertisement providing shop and the location information regarding the first user during the experienceable period for the first user.

Disclosed are a business opportunity information sales server and a method thereof, the server comprising: a data acquisition unit for acquiring, on the basis of an external input, lead transaction data and lead purchaser data relating to a lead, which is product- or service-related business opportunity information for a customer, a learning unit for generating a learning model by performing deep learning on the basis of the lead transaction data and the lead purchaser data; and a prediction unit for preforming, which regard to a predetermined lead purchaser, prediction of at least one of a lead purchaser value indicating a value of contribution to lead sales system by the lead purchaser, a churn rate for the lead sales system, and the level of dormancy for the lead sales system, on the basis of the learning model.

A method for tracking a user across multiple website domains over a computerized network, the method comprising sending a first first-party user identifier to a collector in response to a user accessing a first website; generating a first user prompt in response to the user accessing the first website; receiving a first input from the user in response to the first user prompt; redirecting the user to a centralized domain; sending a second first-party user identifier associated with the user and the centralized domain to the collector; correlating the first first-party user identifier with the second first-party user identifier to determine that the user is associated with both the first first-party user identifier and the second first-party user identifier; and displaying a webpage of the centralized domain or a landing page in response to the correlation.

A method for tracking a user across multiple website domains over a computerized network, the method comprising sending a first first-party user identifier to a collector in response to a user accessing a first website; generating a first user prompt in response to the user accessing the first website; receiving a first input from the user in response to the first user prompt; redirecting the user to a centralized domain; sending a second first-party user identifier associated with the user and the centralized domain to the collector; correlating the first first-party user identifier with the second first-party user identifier to determine that the user is associated with both the first first-party user identifier and the second first-party user identifier; and displaying a webpage of the centralized domain or a landing page in response to the correlation.

In one embodiment, a device obtains content data provided by a social media platform to a user of the social media platform. The social media platform selects the content data for the user based on a behavioral model of the user. The device maintains an artificial intelligence-based model that models associations between the content data and interaction data indicative of interactions between the user and the social media platform. The device selects, using the artificial intelligence-based model, an obfuscation action to lower an accuracy of the behavioral model of the user, based on one or more configuration parameters set by the user. The device initiates performance of the obfuscation action.

In various embodiments, promotions are featured on mobile gaming devices. Data based on which a first promotion associated with a first advertiser may be displayed on a mobile gaming device. The data may include a first set of criteria identifying when the first promotion should be displayed.

According to an aspect there is provided an ambient commerce system. The ambient commerce system may include a sensor at an ambient commerce premises. The ambient commerce system may include a processor coupled to the one or more sensors. The ambient commerce system may include a memory coupled to the processor. The memory may store processor-executable instructions which, when executed, cause the processor to: detect, based on an output of one or more of the sensors, engagement of an entity with a restricted item; identify the entity; obtain at least a portion of a profile for the entity; determine, based on the at least a portion of the profile for the identified entity, eligibility for the entity to acquire the restricted item; and selectively generate, based on the determined eligibility for the entity, a notification on an electronic device associated with the entity.

A conversion timing model is model is configured to predict a likelihood of conversion based on an entity's elapsed time since a qualified entry event and based on a funnel state. The conversion timing model is constructed based on a distribution of the conversion timespans of converters. A notification of an opportunity to expose a candidate entity to networked content is received. A time-based likelihood of conversion for the candidate entity is determined by applying the conversion timing model to the elapsed time. A response to the notification based on the likelihood of conversion for the candidate entity is prepared based on the time-based likelihood of conversion and based on the funnel state. Timely responses may include the selection of customized content or bid values.