System and method for optimizing electronic document layouts
A system and method is provided that ranks and sorts websites, apps, email, or VR environment content in real-time to increase engagement, CTR, conversions, and revenue. A client applies attributes to sections of the digital content. A server system tracks end user inputs and generates optimized layouts for the digital content, such as a webpage. The document layout is ordered or reorganized before or after the document is delivered to the end user.
1. A system for optimizing the layout of an electronic document comprising a database and a processor executing programming logic for interfacing with remote systems, the programming logic configured to provide a content sort service, a track service, and a machine learning process;
where the track service accepts end user request data, where the track service stores the end user request data in the database, and where the track service provides the end user request data to the machine learning process;
where the machine learning process uses the end user request data to generate and update models, where the models are stored in the database;
where the content sort service accepts optimization requests for the electronic document, where the electronic document comprises a plurality of sections, where the content sort service accesses the database to obtain models for the optimization request, where at least one of the models obtained from the database uses a progressively narrowed range of content position randomization to generate an optimized order for the plurality of sections for the electronic document, where the content sort service selects one or more models from the models obtained from the database; where the content sort service applies the one or more selected models to generate an optimized order for the plurality of sections for the electronic document.
2. The system of claim 1 , wherein the content sort service further provides a response to an end user, where the response comprises the optimized order for the plurality of sections for the electronic document.
3. The system of claim 1 , wherein the content sort service further provides a response to a client server, where the response comprises the optimized order for the plurality of sections for the electronic document.
4. The system of claim 1 , wherein each optimization request for an electronic document comprises data indicating that one or more of the plurality of sections of the electronic document are pinned.
5. The system of claim 4 , wherein the pinned sections of the electronic document are ignored by the content sort service.
6. The system of claim 1 , wherein each optimization request for an electronic document comprises a key performance indicator, where the content sort service uses the key performance indicator to select the one or more models obtained from the database.
7. The system of claim 1 , wherein the track service provides the end user request data to the machine learning process via one or more log files.
8. The system of claim 1 , wherein the track service provides the end user request data to the machine learning process via a distributed messaging system.
9. The system of claim 1 , wherein at least one of the plurality of sections of the electronic document comprises a plurality of subsections, where the content sort service further applies the one or more selected models to generate an optimized order for the plurality of subsections.