Travel Query Parameter Inference for Accurate Search Results
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Solution Overview
Problem
Conventional search systems struggle to provide accurate responses to generalized queries in specific subject matter areas, particularly in travel-related searches, requiring users to input specific parameters manually, which is time-consuming and often leads to suboptimal results.
Innovation Solution
A system utilizing machine learning algorithms to optimize travel parameters by processing non-bounded queries, identifying missing parameters, and presenting personalized travel results based on customer popularity and user preferences, leveraging natural language processing and predictive modeling to streamline input and output processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search systems require users to input specific parameters manually, then measurement precision of search queries is improved, but loss of time increases and ease of operation deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing travel data, destinations, and user preferences before the actual search query is executed. Machine learning models are trained in advance on historical travel data to enable automatic parameter generation, so when a user submits a non-bounded query, the system can quickly generate relevant parameters without requiring manual input for each field.
Solution Approach 2:
The system enables self-service by automatically generating search parameters, selecting destinations, and filtering results based on user preferences without requiring manual intervention. The machine learning models autonomously analyze the non-bounded query, infer missing parameters, and present optimized travel results, allowing the system to serve itself in completing the search task.
2Manufacturing precision
If conventional search systems require multiple specific parameters, then manufacturing precision of search results is improved, but device complexity increases
Solution Approach 1:
The system implements multi-functionality by using a single machine learning model framework that handles multiple tasks: generating search parameters, selecting destinations, filtering results, and optimizing presentations. This universal approach consolidates what would otherwise require multiple separate processing systems, maintaining high result accuracy while reducing overall system complexity.
Solution Approach 2:
The system dynamically changes parameters based on the analysis of non-bounded queries and user preferences. Instead of requiring fixed parameter inputs, the machine learning models adaptively generate and adjust parameters such as destination, dates, and travel type, allowing the system to maintain precision while managing complexity through flexible parameter transformation.
3Ease of operation
If machine learning models are used to automatically identify parameters, then ease of operation is improved, but loss of information may increase
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously learn from user interactions, selections, and preferences. When parameters are automatically generated or inferred, the system monitors user responses and adjusts its parameter generation strategy accordingly, ensuring that important information is not lost and that the models improve their accuracy over time in recovering relevant parameters.
Solution Approach 2:
The system performs preliminary analysis of user profiles, historical travel data, and contextual information before generating parameters for non-bounded queries. This advance preparation ensures that when parameters are automatically identified, they are based on comprehensive pre-processed information, minimizing the risk of losing important travel details.
4Productivity
If the system presents filtered travel results based on popularity thresholds, then productivity is improved, but adaptability decreases
Solution Approach 1:
The system applies dynamics by making the result filtering and presentation adaptive rather than static. While popularity thresholds are used to efficiently filter results, the system dynamically adjusts these thresholds and filtering criteria based on individual user preferences, historical behavior, and contextual factors. This allows the system to maintain high productivity through automated filtering while preserving adaptability to different user needs and preferences.
Data Source
AI summary
A system includes at least one processing circuit including at least one memory and one or more processors configured to: identify, via at least one machine learning model, at least one excluded parameter of a non-bounded query relating to a trip; identify, via the at least one machine learning model, a set of travel results that match the query and the excluded parameter by: analyzing, based on search data from one or more users, at least one search result distribution corresponding to at least one parameter associated with at least one trip of the one or more users and relating to the at least one excluded parameter; and identifying the set of travel results based on a value regarding the at least one parameter satisfying a presentation threshold for the at least one search result distribution; and present one or more travel results of the set of travel results.