AI-Driven Page Navigation Recommendations Using Reduced Behavior Models
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Solution Overview
Problem
Modern enterprise web applications with hundreds of pages and features can overwhelm new users, hindering their adoption due to complex navigation options.
Innovation Solution
Utilizing AI-driven neural network models trained on historical application log data to generate intelligent page navigation recommendations, with a lightweight model deployed on user devices for real-time assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise web applications provide comprehensive navigation options with hundreds of pages and features, then functionality and versatility are improved, but user complexity and difficulty of operation increase
Solution Approach 1:
The system automatically analyzes user behavior patterns from application logs and generates personalized navigation recommendations without requiring manual configuration or user input. The AI model self-adapts to individual user needs by processing historical navigation data and predicting optimal next steps, enabling the system to serve itself in creating customized navigation paths.
Solution Approach 2:
The AI model pre-processes and analyzes historical application log data offline to build trained models that are subsequently deployed to user devices. This preliminary training action enables real-time navigation recommendations during actual application use, avoiding the need for complex real-time data processing during user interactions.
2Measurement precision
If AI models are trained on comprehensive historical application log data to improve recommendation accuracy, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system separates the AI model training process from the deployment process. Complex training is performed offline on servers using comprehensive historical data, while simplified trained models are deployed to user devices. This segmentation allows high-accuracy models to be created without burdening end-user devices with complex training computations.
Solution Approach 2:
The system creates and deploys simplified copies of trained AI models to user devices. These lightweight model copies retain the essential predictive capabilities learned during offline training but are optimized for efficient execution on resource-constrained end devices, balancing accuracy with device complexity constraints.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for implementing AI driven application navigation recommendations based on user behavior. An embodiment operates by generating a trained machine learning model using training data obtained based on historical navigation logs corresponding to the web application. The embodiment deploys a reduced machine learning model within an instance of the web application, and the reduced machine learning model is generated by compressing the trained machine learning model. The embodiment then generates the page navigation recommendation using the reduced machine learning model based on an encoded navigation breadcrumb data corresponding to the instance of the web application.