Neural Network Webpage Personalization via Encoder-Decoder
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Solution Overview
Problem
Traditional systems for generating custom content are limited by a fixed number of manually-curated options and testing variables, failing to effectively tailor webpages to individual users, as they rely on fixed grouping and testing methods like A/B or multivariate testing.
Innovation Solution
The system iteratively generates custom content by encoding user-specific data using machine learning models, such as neural networks, to create personalized content, which is then modified based on user feedback until a predetermined threshold of effectiveness is achieved, allowing for an infinite number of variables to be tested for human interaction and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional A/B or multivariate testing methods are used to customize webpages, then some level of personalization is achieved, but the number of variations is limited and the system cannot effectively tailor content to individual users
Solution Approach 1:
The patent replaces traditional mechanical testing systems (A/B testing, multivariate testing) with a neural network-based generative system. Instead of manually creating and testing fixed variations, the system uses machine learning models to automatically generate personalized webpage content tailored to individual users, transitioning from a rigid mechanical approach to an adaptive intelligent system
Solution Approach 2:
The system dynamically changes multiple parameters simultaneously (content, layout, features, formatting) based on user-specific data processed by neural networks. This allows for continuous variation generation rather than discrete pre-defined options, enabling highly personalized content while managing complexity through automated parameter optimization
2Adaptability or versatility
If manually-curated options are used for content customization, then implementation is straightforward, but the system fails to effectively tailor webpages to individual users
Solution Approach 1:
The system enables self-service by allowing the neural network to automatically generate personalized content based on user data without requiring manual curation for each user. The encoder-decoder architecture processes user-specific data and autonomously produces customized webpage content, eliminating the need for human intervention in the content generation process while maintaining high personalization quality
Solution Approach 2:
The patent introduces neural networks as an intermediary between raw user data and final personalized content. The encoder transforms user data into encoded representations, which the decoder then translates into customized webpage features, acting as an intelligent mediator that bridges the gap between user characteristics and personalized content generation
3Productivity
If fixed grouping and testing methods are used, then the system is easy to implement, but it cannot test an infinite number of variables for human interaction and effectiveness
Solution Approach 1:
The system transitions from static fixed grouping to dynamic adaptive grouping through neural networks. The encoder-decoder framework continuously adapts to individual user characteristics and generates dynamic content variations, allowing the system to test an effectively infinite number of variable combinations tailored to each user's preferences and behavior patterns
Solution Approach 2:
The system performs preliminary encoding of user-specific data into compressed representations before content generation. This preliminary processing step enables rapid generation of personalized content by pre-processing user data into a form that the decoder can efficiently transform into customized webpage features, improving both speed and adaptability
Data Source
AI summary
Disclosed embodiments may include a system for generating custom content. The system may generate a first webpage comprising first feature(s). The system may transmit the first webpage for display via a graphical user interface (GUI) of a user device. The system may iteratively, until a predetermined threshold is achieved: receive data corresponding to a first user; generate, by an encoder, encoded data based on the data; generate, by a decoder, second feature(s) based on the encoded data; modify the first webpage to generate a second webpage comprising the second feature(s); transmit the second webpage for display via the GUI of the user device; receive user feedback associated with the second webpage; and determine whether the user feedback exceeds the predetermined threshold. Responsive to the predetermined threshold being achieved, the system may transmit the latest version of the second webpage without requesting additional user feedback.