Facial Expression-Based Product Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in accurately identifying user satisfaction with products from facial expressions and efficiently recommending suitable products based on this information.
Innovation Solution
A device equipped with a camera and a processor that uses a trained AI algorithm to determine user satisfaction from facial expression data, selecting and displaying recommended products from multiple product sets based on this analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If facial expression analysis is used to determine user satisfaction, then user experience understanding is improved, but measurement precision deteriorates due to difficulty in accurately identifying feelings from facial expressions
Solution Approach 1:
The patent segments the facial expression analysis into multiple independent components: face region detection, eye region extraction, eyebrow region extraction, and facial muscle movement analysis. Each component is processed separately to extract specific features (eye closure degree, eyebrow position, muscle tension) that are then combined to determine overall user satisfaction, thereby improving measurement precision through systematic decomposition of the complex analysis task
Solution Approach 2:
The patent introduces intermediate processing steps between capturing facial expressions and determining user satisfaction. These intermediaries include extracting specific facial regions (eyes, eyebrows), calculating quantitative metrics (eye closure degree, eyebrow position coordinates), and analyzing muscle movements. These intermediate representations serve as mediators that transform raw facial image data into structured satisfaction indicators, improving the precision of the overall measurement process
2Measurement precision
If multiple product sets are analyzed for recommendation, then recommendation accuracy is improved, but device complexity increases due to need to manage multiple product sets and their similarities
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing similarity values between all product sets before the recommendation process. The server computes similarity metrics (based on product attributes, categories, and relationships) and stores these pre-computed values. When a user views a product, the system retrieves pre-stored similarity data and user satisfaction information to quickly determine recommendations, avoiding complex real-time calculations and reducing system complexity during operation
Solution Approach 2:
The patent implements a dynamic recommendation mechanism where the system adaptively selects which product sets to recommend based on real-time user satisfaction levels. When satisfaction is high, the system recommends products from sets with higher similarity; when satisfaction is low, it recommends from sets with lower similarity. This dynamic adjustment optimizes recommendation accuracy while managing complexity by selectively processing only relevant product sets based on current user state
Data Source
AI summary
Provided is an artificial intelligence (AI) system for simulating human brain functions such as perception and judgment by using a machine learning algorithm such as deep learning, and an application thereof. Provided is a device and method for recommending products to a user on the basis of facial expression information of the user through the AI system.The method, performed by the device, of recommending products includes: displaying a product selected by the user; obtaining user's facial expression information with respect to the displayed product; determining the user's satisfaction with the displayed product based on the obtained user's facial expression information; selecting a product set to be recommended to the user from among a plurality of product sets based on the determined user's satisfaction; and displaying at least one product included in the selected product set.