A method includes receiving a request and request data associated with a user from a web server and analyzing the request data to identify one or more data gaps associated with the request. One or more third-party services are called to fill at least a portion of the one or more data gaps. A question set is prepared based on determining that the one or more data gaps remain at least partially unfilled. The question set is selected by a machine-learning component trained to adapt a sequence and content of the question set over a plurality of interactions with a plurality of users. The question set is transmitted to the web server for presentation to the user. Data exchanges can be authenticated using tokens.

A method, system, and apparatus provide the ability to personalize a digital channel. A digital channel is provided to multiple users and visitor information at each visit is collected. The visitor information includes data about each visit and multiple content items that are presented. The users are autonomously clustered by segmenting the user population into behavioral groups such that mutual information is maximized between the users in an assigned behavioral group and the content items. Based on the clustering, a model is generated that estimates a score for each interaction between users and content items. The model is updated at a defined interval. Based on the score, content items to recommend to a specific user are determined. The recommendation jointly maximizes an outcome and a learning speed of the model. The personalized digital channel is delivered to the specific user based on the recommended multiple content items.

Systems, devices, and techniques are disclosed for user interest detection for content generation. A set of time series data including user interactions with computer accessible resources may be received. A set of expected event data may be received. Irregular event data may be received. A prediction of user interest in an event, including an identification of the event, a time of the event, and levels of user interest before, during and after the time of the event may be generated from the set of time series data, the set of expected event data, and the set of irregular event data. An item of content may be displayed to a user at a time based on the prediction of user interest in the event.

A method for providing information based on e-commerce according to an embodiment of the present disclosure is performed on a computing device including one or more processors and a memory that stores one or more programs executed by the one or more processors, and includes receiving a product promotion request including product information and requester identification information for a product to be promoted, and selecting a contents creator to create promotional content for the product in response to the product promotion request.

A financial institution computing system includes an account database with a plurality of transaction parameters with respect to a financial account of a customer, a receptiveness metrics circuit structured to extract the plurality of transaction parameters from the account database, the transaction parameters including at least one financial transaction record, and indicative of a mode of the customer, and determine one or more receptiveness metrics attributed to the customer based on the mode of the customer, the one or more receptiveness metrics indicating likelihoods of the customer converting an interaction from the financial institution, wherein the interaction includes an avatar that is an aged version of the customer, and an interaction generation circuit structured to transmit the interaction to the customer at an optimal time based on the one or more receptiveness metrics.

An application framework for mobile devices may provide a variety of application modules directed towards enterprise brand extension. The application modules are organized into five main categories: (1) featured, (2) community, (3) play/engage, (4) media, and (5) shop. The featured category may allow enterprises to push specific content onto its consumers. The community category may allow enterprises to leverage social networks and consumer communities that build and expand around their brands. The play/engage category may allow enterprises to offer compelling value and engaging utility to its customers. The media category may allow enterprises to entertain, inform, and educate consumers about brands through media content. The shop category may allow enterprises to facilitate electronic commerce with its customers. Further application analytics may be utilized by aggregating affiliate, sales, or usage data, etc. to better drive new revenue streams and optimize the return on investment associated with sales, promotion and advertising efforts.

Techniques are provided for anonymizing statistical reports to protect user privacy. In one embodiment, a first request to view an aggregated statistic pertaining to online behavior of multiple users is received. In response to receiving the first request, a plurality of attributes associated with the first request is determined; a function is applied that accepts a seed value and the plurality of attributes to generate a number; a particular noise factor is determined based on the number and a distribution of noise factors; a true value for the aggregated statistic is determined; a noisy value that is different than the true value is determined based on the true value and the particular noise factor; and the noisy value is presented in response to the first request instead of the true value.

Systems and techniques are disclosed for creating segments of users that include baseline users having specified traits and users that are similar to the baseline users. A segment is created by identifying baseline users based on a segment rule that specifies one or more traits of the users to include. The data about the baseline and other users in the dataset is used to extend the segment. A representation of the segment is determined, for example, by determining average values of numeric traits and frequencies of non-numeric trait values of the baseline users in the segment. The representation of the segment is used to determine the similarity (i.e., similarity scores) of users to the segment and ultimately to determine which of the other users, who are not already included in the segment, should be included in the segment based the similarity of their traits to those of the segment representation.

A method, system, and computer program product for inserting a search box into a mobile terminal messaging dialog. Upon receiving a dialog message (e.g., an email message) from a first user device, the method determines the format (e.g., IMAP) of the dialog message in order to insert a similarly-formatted search box. Then, the method modifies the dialog message by inserting the selected search box into the dialog message. The method receives a request from a second user (at a mobile terminal) and the method transmits the outbound dialog message with the inserted search box to the second user's mobile terminal. The second user's mobile terminal supports a web browser, and some dialog messages include pre-populated text in the search box, the pre-populated text based on the dialog message from the first user device. The second user browses using the inserted search box without having to explicitly launch a browser.

Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.