Vehicle Recommendation System Using Transaction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face sub-optimal purchase decisions when buying expensive items online due to ineffective recommendations and lack of accessible transaction information, as existing systems fail to consider financial accessibility and transaction data asymmetry.
Innovation Solution
A computer-implemented method and system that uses machine learning algorithms to determine vehicle desirability scores based on purchase queries and transaction data, providing personalized recommendations to users while assisting merchants with inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If recommendation models are used to suggest items based on website training data, then item recommendations are provided to purchasers, but the recommendations may be ineffective if the items are not financially accessible to the purchasers
Solution Approach 1:
The system segments the recommendation process into multiple components: collecting purchaser financial data separately, analyzing transaction information independently, and integrating these with item recommendations. This allows financial accessibility to be evaluated as a distinct factor rather than being lost in the overall recommendation process.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing purchaser financial information and transaction data before generating item recommendations. This advance preparation ensures that financial accessibility is already determined when recommendations are made, preventing information loss.
2Reliability
If merchants have access to large amounts of transaction information, then advantageous transaction terms can be determined, but purchasers are at a disadvantage due to lack of accessible and digestible information
Solution Approach 1:
The system introduces an intermediary layer that processes merchant transaction information and purchaser financial data, then presents processed, digestible recommendations to purchasers. This intermediary transforms raw transaction data into actionable insights without exposing all underlying information, maintaining reliability while improving accessibility.
Solution Approach 2:
The system implements feedback loops where purchaser responses to recommendations are analyzed and used to refine future recommendations. This continuous feedback mechanism allows the system to learn from transaction outcomes and improve both transaction term optimization and information presentation to purchasers.
Data Source
AI summary
According to certain aspects of the disclosure, a computer-implemented method may be used for regulating vehicle stock. The method may include receiving one or more queries indicative of one or more characteristics of a vehicle for purchase by a user and determining based on the one or more queries indicative of the one or more characteristics of the vehicle, at least one vehicle available for purchase at a location of a merchant. The method may also include determining a quantity of the at least one vehicle purchased and assigning a value to the at least one vehicle based on the quantity of the at least one vehicle purchased and a quantity of received queries about the vehicle. The method may also include transmitting the value to the user, with a recommendation regarding the at least one vehicle available for purchase based on the value.