Dynamic Traveler Profiles for Evolving Preference Inference
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Solution Overview
Problem
Travel planning is arduous due to the variety of options and the evolving nature of user preferences, which existing systems fail to accurately capture and adapt to.
Innovation Solution
A dynamic traveler profile is developed using active and passive interactions across multiple platforms, updated continuously, to infer and adapt to changing preferences, providing personalized travel recommendations and itinerary adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static traveler profile is used, then the system structure is simple, but it cannot accurately capture and adapt to evolving user preferences
Solution Approach 1:
The system transitions from a static traveler profile to a dynamic profile that continuously evolves based on user interactions. The profile is updated in real-time as users interact with travel services, allowing it to adapt to changing preferences automatically without requiring manual intervention.
Solution Approach 2:
The system performs self-learning by automatically analyzing user interactions and updating the traveler profile without external intervention. The machine learning model processes user behavior data and autonomously adjusts the profile to reflect evolving preferences, eliminating the need for manual profile management.
2Ease of operation
If the system requires explicit user input for preferences, then the profile accuracy is high, but the ease of operation decreases
Solution Approach 1:
The system automatically detects and learns user preferences by analyzing their interactions with travel services. Instead of requiring users to manually input preferences, the system observes user behavior patterns, such as search queries, bookings, and engagement data, to infer and update preferences automatically.
Solution Approach 2:
The system continuously monitors user interactions and uses this feedback to refine the traveler profile. User behaviors such as viewing, searching, and booking actions provide feedback signals that the machine learning model processes to accurately detect and update preferences in real-time.
3Measurement precision
If the system collects interaction data from multiple platforms, then the preference inference is more accurate, but the data processing complexity increases
Solution Approach 1:
The system implements a universal data processing framework that handles interaction data from multiple platforms (travel agencies, airlines, hotels, etc.) through a single machine learning model. This multi-functional approach allows the system to aggregate and process diverse data sources using unified algorithms, reducing the complexity of managing separate processing systems for each platform.
Data Source
AI summary
One embodiment relates to a method for dynamic user preference assessment. The method includes receiving a dynamic user profile indicating a plurality of travel preferences for a user. The method includes providing, to the user, a first indication of a first set of one or more of the plurality of travel preferences indicated by the dynamic user profile at a first time. The method includes obtaining interaction data indicating interactions of the user across a plurality of platforms. The method includes processing the interaction data to infer changes to the plurality of travel preferences indicated by the dynamic user profile. The method includes updating the dynamic user profile to include the changes to the plurality of travel preferences.