Accommodation Ranking via Travel Activity Constraints
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Solution Overview
Problem
Accommodation management systems lack integration with travel activity database systems, requiring guest users to manually search for suitable accommodations that align with their travel goals, such as visiting multiple sports venues, which is inefficient and not user-friendly.
Innovation Solution
An accommodation management system that recommends optimal accommodation listings based on user-defined travel activity goals by filtering and ranking listings based on geographic location, transportation modes, and booking availability, using natural language processing to determine constraints and machine-learned models to predict user availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accommodation management systems manually search for accommodations, then users can find suitable listings, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal accommodation rankings based on various travel constraints and activity types. When a user searches, the system retrieves pre-computed results rather than performing manual searches, significantly reducing response time and improving booking efficiency.
Solution Approach 2:
The patent introduces an intermediary ranking system that acts as a mediator between user travel goals and accommodation listings. This intermediary component automatically matches user constraints with suitable accommodations using machine learning models, eliminating the need for manual searching and significantly improving search efficiency.
2Ease of operation
If the system provides personalized recommendations based on multiple constraints, then user experience is enhanced, but system complexity increases
Solution Approach 1:
The system segments the complex recommendation task into distinct modular components: constraint parsing module, machine learning ranking module, accommodation filtering module, and result generation module. Each module handles a specific aspect of the recommendation process, making the overall system more manageable and maintainable while providing personalized recommendations.
Solution Approach 2:
The patent implements a universal machine learning-based ranking system that handles multiple types of travel constraints and activity types through a single unified framework. This multi-functional approach enhances user experience with personalized recommendations while avoiding the need for separate systems for each constraint type, thus controlling system complexity.
3Reliability
If the system integrates with travel activity database systems, then booking suitability is improved, but system integration complexity increases
Solution Approach 1:
The system introduces an intermediary layer that interfaces with travel activity database systems. This intermediary component translates between different data formats and constraint representations, ensuring reliable matching between user travel goals and accommodation listings while isolating the core accommodation management system from integration complexities.
Solution Approach 2:
The patent employs parameter changes by transforming diverse travel activity constraints into a standardized format that the machine learning ranking system can process. This parameter transformation approach improves booking suitability by ensuring accurate constraint matching while simplifying integration with various travel activity database systems through a unified parameter interface.
Data Source
AI summary
A system and a method are disclosed for optimally ranking and indexing accommodation listing information based on a set of constraints corresponding to a travel activity goal input on a client device. In an embodiment, an accommodation management system receives a travel activity goal input by a guest user on a client device with a corresponding set of constraints. Based on the constraints, the accommodation management system determines a set of geographic coordinates corresponding to the travel activity, and further identifies the set of candidate accommodation listings with accommodations within a threshold distance from the geographic coordinates. The accommodation management system filters and ranks the candidate accommodation listings based on the constraints, and sends a recommendation to the guest user for display on the client device which includes one or more of the ranked accommodation listings.