Composite Lodging Listings from Multi-Provider Content
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Solution Overview
Problem
Conventional online booking systems for lodging facilities often provide incomplete or inaccurate information, leading to customer confusion and the need to visit multiple platforms for comprehensive details, as different providers update information independently and present disparate data.
Innovation Solution
A listing generation system that collects and selectively identifies content from multiple providers to create a composite listing, incorporating various types of information such as text, images, and reviews, using machine learning models to optimize content based on user profiles and conversion rates, without requiring providers to share data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple third-party listing providers independently publish and update lodging information, then customers can access listings through various platforms, but information completeness and accuracy deteriorate due to disparate and conflicting data
Solution Approach 1:
The system merges listing information from multiple third-party providers into a single composite listing. The listing generation system collects data from various providers and combines it with supplemental information to create a comprehensive, unified listing that maintains information completeness while preserving accessibility across platforms.
Solution Approach 2:
The listing generation system acts as an intermediary between multiple third-party providers and customers. It receives data from various providers, processes and validates the information, and generates a standardized composite listing that resolves conflicts and fills gaps in the data.
2Productivity
If third-party providers independently update listing information, then listings can reflect current availability and pricing, but customer confusion increases due to conflicting updates across platforms
Solution Approach 1:
The system implements feedback mechanisms to monitor and validate listing information across providers. It tracks changes in pricing, availability, and other parameters, and uses this feedback to ensure consistency in the composite listing while maintaining the ability to reflect current information quickly.
Solution Approach 2:
The listing generation system performs preliminary processing of listing data from multiple providers before presenting it to customers. It pre-validates and reconciles information to prevent conflicts and inconsistencies from reaching the customer, while still maintaining rapid updates.
3Quantity of substance
If customers visit multiple third-party platforms to gather comprehensive information, then they can access diverse listing data, but time consumption increases due to repeated visits
Solution Approach 1:
The system combines diverse listing information from multiple platforms into a single comprehensive composite listing. This allows customers to access the same diversity of information that would otherwise require visiting multiple platforms, significantly reducing time consumption while maintaining information quality.
4Adaptability or versatility
If listing providers operate independently without data sharing, then operational autonomy is maintained, but information accuracy deteriorates due to incomplete data coverage
Solution Approach 1:
The listing generation system serves as an intermediary that maintains provider autonomy while improving information accuracy. It collects data from independent providers without requiring them to share full operational control, and supplements their data with additional information sources to enhance accuracy.
Solution Approach 2:
The system creates composite listings that combine data from multiple independent providers with supplemental information sources. This composite approach maintains the independence of individual providers while achieving higher information accuracy through aggregation and cross-validation of data.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer-readable media for selectively identifying lodging content from listings provided by different listing providers and generating a composite listing that includes content selected across the different providers. For example, systems described herein may include content selection models that are trained to select different content types from listings that are provided by different listing providers based on features of the content and/or users. The composite listing may enable a user to book a stay with a lodging facility based on content presented via different third-party content providers for the same facility.


