Product Matching via Vector Embeddings and Geometric Verification
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Solution Overview
Problem
Current product recognition systems face challenges in accurately matching products across different metadata sources due to variations in human descriptions and visual representations, leading to inaccuracies and poor search results, as they lack robustness in capturing nuances and visual changes.
Innovation Solution
A method that converts product metadata into feature vectors by combining semantic similarity of textual descriptions and visual similarity of images, using neural networks to generate embeddings and validate matches through geometric verification, improving the accuracy of product search and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-defined feature sets are used for product matching, then the system is simple to implement, but it fails to capture nuances and visual variations leading to poor matching accuracy
Solution Approach 1:
The patent replaces traditional mechanical/manual feature definition with automated neural network-based feature extraction. Convolutional neural networks automatically learn visual features from product images, while recurrent neural networks extract semantic features from textual descriptions, eliminating the need for manual feature engineering and significantly improving matching accuracy
Solution Approach 2:
The system transforms product metadata from traditional structured formats into vector embeddings in a continuous vector space. This parameter transformation allows for nuanced comparison by measuring distances between vectors, capturing subtle variations that discrete feature sets miss while maintaining computational efficiency through optimized vector operations
2Reliability
If traditional similarity metrics are used, then computational resources are saved, but recall and accuracy in detecting product variations deteriorate
Solution Approach 1:
The system performs preliminary action by pre-computing and storing vector embeddings for all products in the database before actual search queries. This preprocessing step allows rapid comparison during search by simply computing distances between query vectors and stored vectors, significantly improving recall while reducing real-time computational burden
Solution Approach 2:
The patent transitions from comparing products in traditional categorical dimensions to comparing them in a high-dimensional vector space. This dimensional transformation enables the system to capture complex relationships and nuances in product descriptions and images, dramatically improving search recall by detecting similarities that traditional metrics miss
Data Source
AI summary
A method and system of matching a first product with a second product. The method including converting first product metadata with image metadata and textual data to a first product feature vector. Further, determining a distance between the first product feature vector and a second product feature vector of the second product, the second product feature vector stored in a database of product feature vectors. The distance is compared to a threshold distance, and if the distance is within the threshold distance, validating a match between the first product feature vector and the second product feature vector. The validating further includes geometrically verifying the image metadata of the first product corresponds to image metadata of the second product.