Systems and methods of dynamic page layout using exploration-exploitation models

A computer-implemented method, comprising:

• generating a first interface including a set of content modules comprising a set of generic content modules and a set of dynamic content modules selected from a set of candidate content modules based on a set of ranking weights, each content module in the set of candidate content modules including at least one or more items comprising a link to navigate or obtain a function from the first interface;
• receiving feedback data representative of one or more interactions with the first interface, wherein the feedback data includes positive feedback and negative feedback;
• generating a trained ranking model by inputting a training dataset including a set of contextual data, positive data, negative data, wherein the training dataset is normalized by a normalization module, and wherein the trained ranking module is generated by iteratively revising a set of model parameters;
• generating the set of ranking weights for each content module in the set of candidate content modules, wherein the ranking weights are generated by a context explore-exploit model based on the feedback data;
• modifying the inclusion of the dynamic content modules by adjusting the ranking weights for each content module in the set of content modules based on a variance control parameter, wherein the modification is performed by an exploration module;
• receiving a request for a second interface including a context;
• generating a user segmentation based on a user identifier, context and historical user data;
• generating, by the trained ranking model, a set of ranked content modules for the context, wherein the trained ranking module is configured to receive the user segmentation and a rank of dynamic content modules, wherein the rank of dynamic content modules is based on the user segmentation and the set of ranking weights;
• and
• generating a second interface including a second set of content modules, wherein the second set of content modules includes a predetermined number of top ranked content modules in the set of ranked content modules;
• and
• providing the generated second interface to a user system, wherein a user or operator of the user system may interact with the generated second interface.

A computer-implemented method, comprising:

• receiving a dynamic content module from a set of candidate content modules, the set of candidate content modules including a set of generic content modules and a set of dynamic content modules selected based on a set of ranking weights, each dynamic content module in the set of dynamic content modules including at least one or more items comprising a link to navigate or obtain a function from the first interface;
• initializing the dynamic candidate content module to include a set of default ranking weights;
• generating a first interface including the dynamic candidate content module and the set of generic content modules;
• receiving feedback data representative of one or more interactions with the first interface, wherein the feedback data includes positive feedback and negative feedback;
• generate a trained ranking model by inputting a training dataset including at least a set of contextual data, positive data, and negative data, wherein the trained data set is normalized by a normalization module, and wherein the trained ranking model is generated by iteratively revising a set of model parameters;
• generating the set of updated ranking weights for the dynamic content module, wherein the set of updated ranking weights is generated by a contextual explore-exploit module based on feedback data;
• modifying the inclusion of the dynamic content module in the set of candidate content modules by adjusting the set of ranking weights for each content module in the set of content modules based on a variance control parameter, wherein the modification is performed by an exploration module;
• providing the dynamic content module including the set of updated ranking weights to a ranking process, wherein the ranking process includes using the trained ranking model to receive a user segmentation and a rank of dynamic content modules, wherein the user segmentation is generated based on a user identifier, the context, and the historical user data, and wherein the rank of dynamic content modules is generated based on the user segmentation and the set of ranking weights to rank content modules, wherein the ranking process is configured to select a set of contextual content models for inclusion on a second interface;
• and
• providing the generated second interface to a user system, wherein a user or operator of the user system may interact with the generated second interface.