Virtual Personalization Using GANs for E-commerce Product Visualization
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Solution Overview
Problem
E-commerce platforms face challenges in providing consumers with personalized product suitability insights, particularly for physical items like clothing, as consumers lack interaction with sales associates and limited experience with products before purchase, leading to potential misfits and returns.
Innovation Solution
A system and method that uses a multilabel classifier and generative adversarial neural network to combine user profile data with product data, generating visual representations of products on personalities matching user preferences, presented in an advertising format to enhance product suitability and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional e-commerce platforms display products without personalized visualizations, then the platform structure remains simple and easy to operate, but consumers cannot visualize how products look on them leading to potential misfits and returns
Solution Approach 1:
The system creates virtual copies of products applied to virtual representations of consumers (personas). Instead of requiring physical try-ons, the system generates synthetic images showing how products would look on different consumers based on their profile data, body type, and preferred personalities. This virtual copying approach enables product suitability visualization without complex physical infrastructure.
Solution Approach 2:
The system introduces virtual personas as intermediaries between products and consumers. These personas serve as mediators that combine consumer profile data, body type information, and personality characteristics to create standardized virtual models. The personas act as a bridge that translates product information into personalized visual representations, simplifying the overall system architecture while improving product suitability assessment.
2Reliability
If the system generates personalized visual representations for each user, then product suitability insights are improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores consumer profile data, body type information, and persona characteristics before actual product visualization is needed. By preparing these foundational elements in advance and maintaining them in databases, the system can quickly generate personalized visual representations without performing complex computations in real-time during user interactions.
Solution Approach 2:
The system dynamically adjusts the level of personalization based on user needs and interaction context. Rather than always generating fully customized visualizations, the system can use pre-computed persona-based representations for standard queries and only perform more intensive personalized processing when users provide additional feedback or require higher levels of customization, thus optimizing processing time.
3Manufacturing precision
If the system uses complex AI models like generative adversarial networks, then visual quality and personalization improve, but device complexity and computational requirements increase
Solution Approach 1:
Instead of requiring complex real-time rendering of every possible product-persona combination, the system uses pre-generated and pre-processed visual data copies. The generative adversarial network creates a library of standardized visual representations that can be quickly retrieved and combined with product information, reducing the computational burden compared to generating entirely new visualizations for each query.
Solution Approach 2:
The AI processing pipeline is segmented into distinct stages: data collection and storage, persona generation, product matching, and visual composition. By dividing the complex AI model into modular components that process information in separate stages, the system reduces the computational complexity required at any single point in time while maintaining high visual quality through coordinated processing across segments.
Data Source
AI summary
In an approach for identifying a user's interest in a media object and one or more personalities and presenting the user with a visual of how the media object looks on a particular personality, a processor receives a request to determine suitability of the media object for the user. A processor presents the media object to a multilabel classifier to be matched to a set of profile data of the user and a set of data of one or more personalities. A processor receives an output instruction from the multilabel classifier to combine the media object with a particular personality of the one or more personalities. A processor generates a combined media object of the media object with the particular personality using a generative adversarial neural network. A processor inserts the combined media object into an advertising template to generate an advertisement. A processor presents the advertisement to the user.