Travel Search Query Inference for Missing Parameter Accuracy
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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 search queries by extracting meaningful information from non-bounded user inputs, such as 'Weekend flights to California,' and generating personalized travel results based on predictive modeling and natural language processing, thereby simplifying input parameter entry and refining output presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search systems require users to input specific parameters manually, then search precision can be improved, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically identifying and filling in missing travel parameters (such as departure date, destination, traveler count) before the user completes their search. This pre-processing of search parameters reduces the time users need to spend manually inputting information while maintaining search precision through structured parameter completion.
Solution Approach 2:
The search system provides self-service functionality by automatically analyzing unstructured user input (like natural language queries) and converting it into structured search parameters without requiring manual user intervention. The system serves itself by autonomously completing the parameter extraction and validation process, thereby reducing user effort and time consumption.
2Measurement precision
If conventional search systems require detailed parameter input, then result accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system introduces an intermediary processing layer that translates unstructured natural language user input into structured search parameters. This intermediary layer (comprising NLP and machine learning models) acts as a bridge between casual user queries and the rigid parameter requirements of the search engine, maintaining result accuracy while dramatically improving ease of operation.
Solution Approach 2:
The system dynamically changes parameters from unstructured natural language forms into structured, standardized parameter formats automatically. By transforming parameter representations (from free-text to structured fields like departure_date, destination, traveler_count), the system maintains the precision needed for accurate results while simplifying the user interface to accept more natural, easier input.
3Ease of operation
If the system processes unbounded queries with missing parameters, then ease of operation improves, but the ability to provide accurate responses deteriorates
Solution Approach 1:
The system performs preliminary analysis of unbounded queries to identify missing parameters and uses machine learning models to predict appropriate values based on user profiles, historical data, and contextual information. This pre-processing step ensures that even when users provide incomplete queries, the system can generate accurate responses by filling gaps before executing the search.
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from user interactions and search results to improve parameter prediction accuracy. By incorporating feedback loops that analyze user behavior patterns and search outcomes, the system maintains high response accuracy even when processing unbounded queries with missing parameters, thereby improving reliability without sacrificing ease of operation.
Data Source
AI summary
A system includes at least one processing circuit including at least one memory and one or more processors configured to: obtain, via a user interface, a non-bounded query relating to a travel trip, the non-bounded query excluding at least one parameter associated with the travel trip; identify, via a first machine learning model, the at least one excluded parameter based on the non-bounded query; identify, via a second machine learning model and from among a first set of travel results that match the non-bounded query and the excluded parameter, a second set of the travel results that meet a presentation threshold of customer popularity based on at least one of a location selected by one or more users or a time selected by the one or more users; and present, via the user interface, one or more travel results of the second set, and the presentation threshold.