Travel Recommendation Engine Using Image and Transaction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data analysis techniques fail to provide customized travel recommendations based on personal or subjective user information, limiting their ability to accurately prepare individuals for upcoming trips.
Innovation Solution
A system utilizing machine learning models to analyze transaction, image, and travel data to generate personalized travel item recommendations, which can be purchased and delivered to the user's destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data analysis techniques are used, then objective analysis can be provided, but personalized recommendations cannot be generated
Solution Approach 1:
The patent combines multiple data sources including transaction data, image data, and travel data into a unified analysis framework. This merging allows the system to process both objective transactional information and subjective visual preferences together, enabling personalized recommendations that traditional single-source analysis cannot provide.
Solution Approach 2:
The patent introduces computer vision technology as an intermediary to extract meaningful information from image data. This intermediary layer transforms subjective visual inputs into structured data that can be analyzed alongside transactional data, bridging the gap between personal preferences and objective analysis.
2Measurement precision
If multiple data types are collected and analyzed, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct processing stages: transaction data collection, image data acquisition, computer vision processing, and recommendation generation. Each segment handles a specific type of data or processing function, making the overall complex system manageable through modular organization.
Solution Approach 2:
The patent employs computer vision technology as an intermediary processing layer that automatically extracts features from image data. This intermediary handles the complexity of visual analysis, presenting simplified structured information to the recommendation engine without requiring users to manually process complex visual data.
3Loss of time
If automated recommendation system is implemented, then user preparation time improves, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary data collection and analysis by gathering transaction data, image data, and travel data before the user needs recommendations. The system processes this data in advance using computer vision and machine learning models, so that when recommendations are needed, they are already prepared and can be delivered quickly without intensive real-time processing.
Data Source
AI summary
Disclosed embodiments may include a system for providing customized recommendations via data analysis. The system may receive transaction data associated with a user. The system may cause a user device associated with the user to display a notification prompting the user to provide image data. The system may receive the image data. The system may identify, from the image data via computer vision, first object(s). The system may generate, via an MLM, first item recommendation(s) based on the first object(s). The system may cause the user device to display, via the GUI, the first item recommendation(s). The system may receive type(s) of travel data. The system may generate, via the MLM, second item recommendation(s) based on the type(s) of travel data. The system may transmit, to a merchant system, a request to purchase at least one item at a predefined location based on the second item recommendation(s).