Vehicle Recommendation System Using Image Interaction Analysis
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Solution Overview
Problem
Users face challenges in conveying their unique preferences for vehicle features when using traditional online search interfaces, leading to increased time spent researching and potential purchaser dissatisfaction.
Innovation Solution
A computer-implemented method and system that provides a vehicle recommendation to a user by obtaining vehicle images, identifying user-selected images, extracting first- and second-level attributes, determining attribute values, and generating a vehicle recommendation based on user preference data using a trained machine learning algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional online search interfaces are used for vehicle shopping, then users can access vehicle information, but users struggle to convey their unique preferences and spend significant time researching
Solution Approach 1:
The system automatically analyzes user behavior data from interactions with vehicle images and attributes to generate recommendations without requiring users to manually specify their preferences. The algorithm self-adjusts based on user selections, eliminating the need for users to actively convey their preferences through complex filters or forms.
Solution Approach 2:
The system implements a feedback loop where user interactions with vehicle images and attributes are continuously monitored and fed back into the recommendation algorithm. This allows the system to learn from user behavior patterns and refine recommendations in real-time, reducing the time users need to spend researching by adapting to their evolving preferences during the shopping process.
2Adaptability or versatility
If traditional rigid online search interfaces are used, then users can search for vehicles, but users experience dissatisfaction and disengagement due to inability to express unique preferences
Solution Approach 1:
The recommendation system is designed to be dynamic rather than static. It continuously adapts its recommendation criteria based on real-time analysis of user interactions with vehicle images and attributes. The system evolves its understanding of user preferences during the shopping session, making it highly adaptable to individual users while maintaining reliable satisfaction through personalized recommendations.
Solution Approach 2:
The system changes the parameters of the search and recommendation process based on user behavior. Instead of using fixed search criteria, the system dynamically adjusts the weight and importance of different vehicle attributes based on what the user interacts with, transforming a rigid interface into a flexible, preference-adaptive system that maintains user engagement and satisfaction.
Data Source
AI summary
A computer-implemented method for providing a vehicle recommendation to a user may include: obtaining one or more vehicle images via a device associated with the user; identifying one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images performed by the user via a user interface; identifying one or more first-level attributes from the one or more user-selected images; obtaining one or more vehicle identifications from the one or more user-selected images; determining one or more second-level attributes based on the one or more vehicle identifications; determining a value of each of the one or more first-level attributes and the one or more second-level attributes; determining the vehicle recommendation based on the value; and transmitting, to the device associated with the user, a notification indicating the vehicle recommendation.