Vehicle Recommendation System Using Entropy-Based Attribute Selection
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Solution Overview
Problem
Online vehicle shopping interfaces often present consumers with large, cumbersome lists of vehicles, making it difficult for users to find desired vehicles due to varying inventory presentation across different websites.
Innovation Solution
A method and system using artificial intelligence to determine frequency distributions and normalized entropy of vehicle attributes, selecting key attributes for user inquiry to efficiently narrow down vehicle recommendations based on user preferences, and presenting a list of recommended vehicles filtered by user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large lists of vehicles are presented to consumers, then more vehicles are available for selection, but the difficulty of finding desired vehicles increases
Solution Approach 1:
The system segments the vehicle selection process into multiple stages by asking users a series of targeted questions about specific attributes (make, model, year, price range, etc.). Each question narrows down the candidate set, transforming the overwhelming task of reviewing large lists into a structured, step-by-step filtering process that reduces cognitive load and improves ease of operation.
2Adaptability or versatility
If multiple websites vary in inventory presentation, then more options are available, but consistency and ease of use across platforms decreases
Solution Approach 1:
The system provides a standardized, consistent user experience across different websites and platforms by automatically adapting the inquiry sequence to the user's device and preferences. Rather than requiring users to learn different interfaces for different sites, the system self-adjusts to present questions in a uniform, easy-to-understand manner, ensuring consistent ease of operation while maintaining versatility.
3Measurement precision
If consumers manually sort through large inventory lists, then comprehensive search is possible, but time consumption increases
Solution Approach 1:
The system uses feedback from user responses to dynamically adjust the search process. After each answer to an attribute question, the system updates the candidate set and presents the next relevant question, creating an adaptive feedback loop that efficiently narrows down options. This interactive approach maintains comprehensive search capabilities while significantly reducing the time required compared to manual sorting through entire inventory lists.
4Measurement precision
If the system asks many questions to narrow down preferences, then recommendation accuracy improves, but the number of inquiries increases
Solution Approach 1:
The system performs preliminary analysis of vehicle attributes and their frequency distributions before presenting questions to users. By pre-calculating which attributes provide the most information gain and are most relevant to current inventory, the system determines the optimal question sequence in advance. This preliminary preparation enables the system to achieve high recommendation accuracy through a minimized, strategically ordered set of inquiries rather than asking many questions sequentially.
Data Source
AI summary
Systems and methods are disclosed for providing purchase recommendations. According to some examples, a method may include: determining respective frequency distributions of a plurality of vehicle attributes, the frequency distributions being determined based on occurrences of values of the plurality of vehicle attributes in a set of vehicles; selecting a vehicle attribute from the plurality of vehicle attributes based on the frequency distributions of the plurality of vehicle attributes; transmitting, to a user device, an inquiry for user preference regarding the selected vehicle attribute; receiving, from the user device, a response indicating the user preference; and presenting, to the user device, a recommendation of one or more vehicles determined based on the received response.