Dynamic Web Page Generation Using Predictive User Modeling
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Solution Overview
Problem
Current recommendation systems on e-commerce websites have limited accuracy due to their reliance on hard-coded features and fail to fully customize the user experience, as they only consider a limited set of product characteristics, neglecting factors like brand, price, and return policy, and do not adapt web page designs to individual user preferences.
Innovation Solution
A system that uses a predictive machine learning engine to analyze user interaction data from both the website and external internet sources, such as social media and product reviews, to select and dynamically generate customized web page designs, layouts, and content, including dynamic pricing and promotions, tailored to individual users based on predicted key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current recommendation systems use hard-coded features and limited product characteristics, then the system complexity is reduced and easier to implement, but the prediction accuracy and user experience customization are limited
Solution Approach 1:
The patent applies parameter changes by transitioning from hard-coded features to dynamic feature selection based on user profiles and contextual data. The system automatically adjusts which product characteristics are considered (e.g., brand, price, return policy) based on individual user preferences and behavior patterns, enabling high-accuracy predictions without manual configuration of every possible parameter.
Solution Approach 2:
The recommendation system performs self-service through automated machine learning models that continuously learn from user interactions. The system autonomously identifies relevant product characteristics and adjusts recommendations without requiring manual programming of complex decision logic, thereby achieving high accuracy while reducing the burden of manual system configuration.
2Measurement precision
If the system considers only a limited set of product characteristics, then the processing time and computational resources are reduced, but the accuracy of user preference prediction decreases
Solution Approach 1:
The patent applies local quality by customizing the set of product characteristics considered for each individual user based on their specific preferences and behavior patterns. Rather than uniformly processing all product features for every user, the system identifies and processes only the locally relevant characteristics (e.g., brand for fashion enthusiasts, price for budget-conscious shoppers) for each user profile, optimizing the balance between processing time and prediction accuracy.
Solution Approach 2:
The system implements partial action by selectively processing only the necessary product characteristics for each user based on their profiles and interaction history. The machine learning models determine which features are most predictive for each user segment, allowing the system to process a subset of relevant features rather than all possible characteristics, thereby reducing processing time while maintaining high prediction accuracy through targeted feature selection.
3Productivity
If web pages are customized for each individual user, then user engagement and conversion rates improve, but the complexity of web page generation and delivery increases
Solution Approach 1:
The patent applies segmentation by dividing the population into distinct user segments or profiles based on shared characteristics, preferences, and behaviors. Rather than creating entirely unique web pages for each individual, the system generates customized web pages for each user segment using machine learning models, thereby achieving high engagement rates while reducing the complexity of personalized page generation through group-based customization.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting web page elements (such as product recommendations, layout, and content prioritization) based on user profile parameters and contextual data. The machine learning models automatically modify page parameters in real-time based on user interactions, enabling high-conversion customized pages without requiring manual design and development of each possible variation, thus reducing generation complexity.
Data Source
AI summary
A system includes a computer storage device to store a first and second set of information about individuals, and user interface design components. A computer server is coupled to the computer storage and is programmed to receive a request for a webpage from an individual's device and analyze the first and second set of information to predict differences in at least one key performance indicator for that individual. The server is further programmed to automatically select a user interface design component for presentation to the individual, automatically generate and transmit a first customized webpage with the selected user interface design component to the device, and in response to an interaction with the selected user interface design component on the first customized webpage, automatically generate and transmit a second customized webpage to the device to create a customized page flow.