Private Profile ML Models for E-commerce Personalization
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Solution Overview
Problem
E-commerce websites face challenges in providing accurate product recommendations due to reliance on text-based user preferences from buying and browsing history, lack of AI-based private profile models, centralized storage of user preferences, and difficulty in tracking real-time preferences, which can lead to inaccurate recommendations and privacy concerns.
Innovation Solution
Constructing private profile models using machine learning techniques that are stored on user devices, analyzing user interactions through object detection and text classification, and enabling e-commerce websites to access these models via APIs for personalized product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user preferences are stored centrally on e-commerce servers, then the system can easily access and process recommendation data, but user privacy is compromised and data security risks increase
Solution Approach 1:
The patent extracts user preference data from centralized e-commerce servers and stores it locally in private profile models on user devices. This extraction moves the sensitive data out of the centralized system, reducing privacy risks while maintaining accessibility through API integration.
Solution Approach 2:
The patent introduces an intermediary layer (API interface) that enables e-commerce platforms to access private profile models stored locally on user devices without directly handling sensitive user data. This mediator allows easy access to preferences while preserving user privacy and data security.
2Ease of manufacture
If traditional text-based analysis of browsing history is used, then the system is simple to implement, but recommendation accuracy is insufficient
Solution Approach 1:
The patent replaces traditional text-based mechanical analysis methods with AI-based machine learning models. These models use object detection, color detection, and supervised text classification algorithms to accurately analyze product images and descriptions, significantly improving recommendation precision while maintaining implementation feasibility through modular architecture.
3Adaptability or versatility
If real-time tracking of user interactions is implemented, then personalized recommendations can be provided dynamically, but system complexity and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of user interactions and trains private profile models in advance on user devices. By pre-processing interaction data and building personalized models locally before needed, the system enables real-time personalized recommendations without requiring complex real-time computational processing on servers.
Solution Approach 2:
The patent implements self-service by enabling user devices to autonomously train and store private profile models locally using JavaScript injection and machine learning algorithms. This self-contained approach allows real-time personalization without increasing server-side system complexity, as each device independently manages its own recommendation model.
4Adaptability or versatility
If multiple e-commerce platforms need to access user preferences, then user experience consistency improves, but data security and access control become more difficult
Solution Approach 1:
The patent creates universal private profile models that can be accessed by multiple e-commerce platforms through a standardized API interface. The models are stored locally on user devices and can serve multiple platforms simultaneously, providing consistent personalized experience across different e-commerce sites while maintaining a single secure storage location.
Solution Approach 2:
The patent uses an API intermediary layer that securely manages access to private profile models by multiple e-commerce platforms. The API acts as a controlled gateway that authenticates platforms and regulates data access, enabling cross-platform compatibility while maintaining robust security controls and preventing unauthorized access to user preferences.
Data Source
AI summary
In an approach for constructing private profile machine learning models for recommending products to a user, a processor gathers user data associated with interactions of the user on an ecommerce website. A processor analyzes the user data using machine learning (ML) techniques. A processor trains a private profile ML model on the analyzed user data, wherein the private profile ML model is stored on a private storage of the user. A processor predicts a product recommendation using the private profile ML model. A processor outputs the product recommendation.