Probabilistic Item Matching via Embedding Vectors
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Solution Overview
Problem
Online platforms face challenges in accurately matching and pricing items for sale, leading to confusion for buyers and sellers, with existing technologies failing to provide reliable search results and pricing strategies within service constraints.
Innovation Solution
Implementing a system that uses probabilistic item matching and search biasing, incorporating natural language processing, image recognition, and neural networks to identify and price items by analyzing user input, backend databases, and external sources, while also considering factors like shipping distance and market trends to optimize search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search and matching methods are used, then system simplicity is maintained, but item matching accuracy and search result reliability deteriorate
Solution Approach 1:
The patent segments the item matching process into multiple independent components: extracting item attributes, generating embeddings, calculating similarity scores, and ranking results. Each component can be processed separately and combined, improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between raw item attributes and similarity calculations. These embeddings transform discrete item features into continuous vector spaces, enabling more accurate and nuanced matching while abstracting the complexity of direct attribute comparison.
2Reliability
If comprehensive item analysis is performed, then matching reliability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by pre-computing embeddings for items in the database and storing them. When a search query arrives, the system only needs to compute the query embedding and compare it against pre-computed item embeddings, significantly reducing processing time while maintaining comprehensive analysis quality.
Solution Approach 2:
The patent implements similarity thresholding and top-k ranking to perform partial action - only processing and returning the most relevant matches above a certain threshold. This avoids exhaustive analysis of all items while maintaining high reliability for the most important results.
3Measurement precision
If multiple search criteria are considered, then search result relevance is improved, but search complexity increases
Solution Approach 1:
The patent creates a universal embedding-based search framework that can handle multiple search criteria (item attributes, categories, prices, etc.) through a single unified similarity calculation mechanism. This multi-functional approach maintains operational simplicity while improving result relevance by considering diverse criteria simultaneously.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for probabilistic item matching and searching. A system including at least one processor may be configured to receive a data point relating to a specific item, generate a database query based on the data point, and receive a response to the database query. The response may include multiple candidate items relating to the specific item. The system may be further configured to receive a first input relating to the specific item and generate a probability score for at least two candidate items of the multiple candidate items in the response, based on at least the second input. The system may be further configured to select a selected item from the candidate items, based on the probability score for the selected item. The system may be further configured to output a reference to, or value representing, the selected item.