Mixture Queries for Split Stay Lodging Search
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Solution Overview
Problem
Conventional lodging search services face challenges in providing accurate and timely pricing and availability information due to query limitations, stale data issues, and inefficient user interfaces, particularly when booking split stays, which can lead to suboptimal results and user dissatisfaction.
Innovation Solution
Implementing a system that generates mixture queries based on user-specified criteria, utilizing a local database of 'live' pricing and availability information to identify and book split stays on behalf of the user, while considering amenities and user preferences, thereby reducing the need for multiple queries and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If querying is used to obtain lodging information, then accuracy and up-to-date information is improved, but response time increases and query frequency is limited
Solution Approach 1:
The system performs preliminary actions by generating mixture queries that break down the original lodging search into multiple smaller time-period queries. This allows the system to proactively gather accurate pricing and availability information from multiple providers before the user needs results, storing them in a cache for rapid retrieval without requiring a single lengthy query that would exceed frequency limits or take excessive time.
Solution Approach 2:
The system segments the original lodging search query into multiple smaller sub-queries, each targeting a specific time period within the overall stay dates. This segmentation allows the system to retrieve accurate information from multiple providers efficiently by making several quick queries rather than one slow query, thereby maintaining accuracy while reducing overall response time.
2Loss of time
If a lodging cache is used to improve response time, then response time is reduced, but data may become stale and accuracy decreases
Solution Approach 1:
The system implements periodic action by systematically updating the lodging cache at scheduled intervals and using mixture queries to refresh data from multiple providers. This periodic refresh mechanism ensures that the cache contains up-to-date information while still providing fast response times, as the system proactively maintains cache freshness rather than passively serving stale data.
Solution Approach 2:
The system uses feedback mechanisms to monitor cache freshness and trigger updates when data becomes stale. By implementing feedback loops that track when cache entries were last updated and comparing against freshness thresholds, the system can automatically refresh cached lodging information from multiple providers, ensuring accuracy is maintained without requiring users to wait for real-time queries.
3Ease of operation
If conventional queries are used for whole stay, then simplicity is maintained, but optimal pricing for split stays is missed
Solution Approach 1:
The system applies dynamics by automatically adapting the query strategy based on the user's stay duration and pricing patterns. Instead of always using simple conventional queries, the system dynamically determines when to apply mixture queries for split stays by analyzing pricing data from multiple providers and detecting patterns that suggest potential savings from splitting the reservation. This maintains simplicity for straightforward cases while optimizing pricing when opportunities are identified.
Solution Approach 2:
The system changes parameters by modifying the query structure from a single conventional query to multiple mixture queries when split stay opportunities are detected. By automatically adjusting the number and timing of queries based on pricing data analysis, the system can identify optimal split stay configurations that conventional single queries would miss, thereby improving pricing optimization while maintaining ease of operation through automation.
4Measurement precision
If multiple queries are performed for split stays, then pricing optimization is improved, but user input and interactions increase
Solution Approach 1:
The system implements self-service by automatically performing the complex task of generating and executing multiple mixture queries for split stay optimization without requiring user intervention. The system autonomously analyzes pricing data, determines optimal split configurations, executes the necessary queries, and presents results to the user. This eliminates the need for users to manually input multiple search parameters or interact with multiple providers, thereby maintaining pricing optimization while preserving ease of operation.
Data Source
AI summary
Approaches to improving mixture queries and booking split stays are described herein. According to some aspects, a user utilizes an exploration tool to search for available lodging. A mixture query is generated based on the stay information received from the user and potential split stays are identified and presented to the user. In some aspects, the split stays are identified based on user-specified split stay criteria, search results are obtained from a local database of “live” pricing and availability information, and a selected split stay option is booked on behalf of the user. Example split stay criteria includes criteria pertaining to an amenity or characteristic of the available lodgings or the available rooms.