Transformer Vehicle Grouping for Accurate Similarity Recommendations
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Solution Overview
Problem
Existing vehicle recommendation systems fail to efficiently group similar vehicles based on mathematical relationships, leading to inaccurate suggestions and a challenging experience for customers searching for related cars.
Innovation Solution
A vehicle recommendation system that uses a bidirectional transformer architecture with self-attention neural networks to analyze user browsing history and demographic data, creating vehicle groups with balanced variance, and predicts similar vehicles based on mathematical relationships and inventory data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle recommendation systems use basic search filters (make, model, year), then the system is simple to operate, but it fails to accurately identify and present similar vehicles to customers
Solution Approach 1:
The patent segments vehicles into distinct groups based on multiple attributes (make, model, year, price range, fuel type) rather than treating them as individual items. This segmentation allows the system to identify patterns and similarities between vehicles more effectively, improving recommendation accuracy while maintaining manageable system complexity through structured data organization
Solution Approach 2:
The patent transitions from traditional one-dimensional search filters to a multi-dimensional recommendation approach by incorporating numerous vehicle attributes simultaneously. This dimensional expansion enables the system to capture nuanced similarities between vehicles that basic filters miss, thereby improving measurement precision without overwhelming complexity
2Measurement precision
If the system groups vehicles using broad industry classifications (year, make, model), then the grouping process is simple, but the resulting vehicle groups are not similar enough for accurate recommendations
Solution Approach 1:
The patent applies segmentation by dividing the vehicle catalog into multiple attribute dimensions (make, model, year, price, fuel type) and creating groups based on combinations of these segments. This allows for precise similarity identification while keeping the grouping process manageable through systematic attribute breakdown
Solution Approach 2:
The patent changes the parameters used for vehicle grouping from broad industry classifications to a comprehensive set of specific attributes including make, model, year, price range, and fuel type. This parameter expansion enables more accurate similarity identification while maintaining ease of manufacture through automated processing of structured data
3Measurement precision
If the system relies solely on user browsing history for recommendations, then the system is easy to implement, but it fails to provide accurate suggestions when browsing history is limited or unavailable
Solution Approach 1:
The patent makes the recommendation system universal by enabling it to function effectively with multiple data sources (browsing history, demographic data, vehicle attributes) rather than relying on a single data type. This multi-functionality allows the system to maintain high accuracy whether browsing history is abundant or scarce, while data processing complexity remains manageable through standardized processing pipelines
Solution Approach 2:
The patent applies preliminary action by pre-processing and structuring vehicle data into standardized groups and attributes before recommendation generation. This preparation enables the system to quickly and accurately generate recommendations even when user browsing history is limited, as the vehicle data is already organized in a way that facilitates rapid similarity matching
Data Source
AI summary
The present disclosure is generally directed to recommending vehicles to a user. a method for recommending vehicle groups includes receiving browsing history of a user. The browsing history includes vehicle click data of the user. The method further includes determining one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data, providing the one or more input vehicle groups to a ML model, and receiving, from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups.