Vector Dimensionality Reduction for Business Similarity Queries
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Solution Overview
Problem
Determining business similarity is challenging due to limitations in available data and comparison methods, particularly with demographic information being unreliable, incomplete, or resource-intensive for computer calculations.
Innovation Solution
A method involving the representation of financial data as vectors, applying dimensionality reduction techniques to create compact vectors, and generating a similarity index for efficient computation and query response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If demographic information is used to determine business similarity, then comparison can be performed with available data, but the data may be unreliable, incomplete, or out of date
Solution Approach 1:
The patent introduces an intermediary mapping system that transforms business attributes into vector representations. Instead of directly comparing raw demographic data (which may be unreliable), the system uses vector embeddings as an intermediary layer that captures semantic relationships and patterns, thereby improving reliability while handling incomplete data gracefully through the mathematical properties of vector spaces.
Solution Approach 2:
The patent changes the parameter representation from discrete demographic categories to continuous vector dimensions. By transforming business attributes into high-dimensional vector spaces, the system can capture nuanced similarities and differences that categorical comparisons miss, improving reliability through richer parameter representations that better reflect actual business similarities.
2Measurement precision
If comprehensive demographic data collection is performed to improve similarity accuracy, then more comparison dimensions are available, but calculations become strenuous and resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-computing vector embeddings for businesses and storing them in advance. When similarity queries are made, the system performs efficient vector comparisons using pre-prepared representations rather than recalculating from raw data each time. This preliminary vectorization step enables fast, resource-efficient similarity searches while maintaining high measurement precision through the use of sophisticated embedding models.
3Productivity
If vector dimensionality reduction is applied to improve computational efficiency, then processing resources are reduced, but potential loss of information may occur
Solution Approach 1:
The patent carefully manages parameter changes by selecting optimal dimensionality reduction techniques that preserve essential business characteristics while reducing computational complexity. The system transforms high-dimensional business attribute spaces into lower-dimensional vector representations that maintain the critical similarity relationships needed for accurate comparison, achieving both efficiency and information retention through mathematically optimized parameter transformations.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for determining similarities between businesses. One example method generally includes receiving a similarity query and receiving transaction data associated with a plurality of businesses for comparing the plurality of businesses. The method further includes generating a set of vectors representing the plurality of businesses based on the transaction data and generating a set of compact vectors based on the vectors by applying a dimensionality reduction technique. The method further includes generating based on the set of compact vectors, a similarity index and determining a response to the similarity query using the similarity index.