Personalized Vehicle Recommender System Using Neural Network Enrichment
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Solution Overview
Problem
Current recommender systems are inadequate for vehicle purchases due to the complexity of vehicle market dynamics, limited user action data, and asymmetry of information, making it difficult to provide accurate and personalized vehicle recommendations that account for driving habits, financial constraints, location, and preferences.
Innovation Solution
A personalized vehicle recommender system utilizing a neural network that generates recommendations by enriching driver profile data with location and social characteristic data, and combining this with vehicle attribute data to provide tailored suggestions, incorporating deep learning methods for adaptive and flexible recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommender systems (collaborative filtering or content-based filtering) are used for vehicle recommendations, then the system structure remains simple, but the recommendation accuracy deteriorates due to limited user action data and asymmetry of information in the vehicle market
Solution Approach 1:
The system segments the recommendation task into multiple independent modules: user behavior analysis module, vehicle attribute analysis module, market dynamics module, and recommendation generation module. Each module processes specific aspects independently, allowing complex analysis without overwhelming system complexity. The segmentation enables parallel processing and modular improvement of individual components.
Solution Approach 2:
The patent introduces an intermediary layer of vehicle market dynamics modeling that mediates between user preferences and vehicle recommendations. This intermediary layer captures the complex relationships in the vehicle market (depreciation, supply-demand, seasonality) and translates them into factors that influence recommendations, thereby improving accuracy without directly complicating the core recommendation engine.
2Adaptability or versatility
If more user data and vehicle attributes are collected to improve personalization, then recommendation relevance improves, but information processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing vehicle attributes, user profiles, and market dynamics data in structured formats before recommendation generation. User behavior patterns are pre-analyzed and vehicle market conditions are pre-modeled, so that during actual recommendation requests, the system only needs to retrieve and combine pre-computed results rather than processing raw data from scratch.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weight and detail level of different data sources based on recommendation context. For example, when user history is rich, the system weights behavioral data higher; when vehicle market conditions change rapidly, it increases the weight of market dynamics parameters. This adaptive parameter adjustment optimizes processing efficiency while maintaining personalization quality.
3Reliability
If the system accounts for vehicle market dynamics (depreciation, supply-demand, seasonality) in recommendations, then recommendation practical value improves, but the model complexity increases
Solution Approach 1:
The system implements dynamics by modeling vehicle market conditions as time-varying parameters rather than static attributes. Vehicle depreciation is modeled as a function of age and mileage, supply-demand relationships are updated based on current market data, and seasonal effects are captured through time-dependent weighting. This dynamic modeling approach captures real market behavior while using parametric models that remain computationally tractable.
Data Source
AI summary
A vehicle recommender system includes a storage system storing a trained neural network trained to generate a vehicle recommendation, a vehicle database comprising vehicle attribute data, and a driver database comprising driver profile data. A processing system communicates with the storage system and is configured to generate enriched driver profile data by comparing user-generated driver profile data to the location characteristic data and social characteristic data. The processing system calculates a driver attribute target value for each vehicle attribute category in a list of vehicle attribute categories based on the driver profile data, and then generates, using the trained neural network, at least one vehicle recommendation based on at least the vehicle attribute data and the driver attribute target values.