Dynamic Flight Search Filters via Itinerary Clustering
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Solution Overview
Problem
Conventional online travel booking sites require users to manually apply standard filters to narrow down numerous flight itineraries, which can be time-consuming and may lead to overlooking the best options due to the lack of meaningful division points in search results.
Innovation Solution
A method that involves clustering flight itineraries based on features like price, duration, and airline, and generating dynamic filters corresponding to these clusters, allowing users to quickly identify suitable flights by presenting meaningful groupings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually apply standard filters to narrow down flight itineraries, then they can filter the results, but the process is time-consuming and may lead to overlooking the best options
Solution Approach 1:
The system performs preliminary clustering of flight itineraries into meaningful groups based on multiple features (price, duration, airline, stops) before the user needs to select. This pre-organization of data into clusters with representative filters allows users to immediately see structured options rather than raw unfiltered results, significantly reducing the time and effort needed for manual filtering while ensuring comprehensive coverage of best options.
2Ease of operation
If conventional standard filters are used, then filtering is possible, but there are no meaningful division points in search results
Solution Approach 1:
The system segments flight itineraries into distinct clusters based on meaningful divisions in the data (e.g., price ranges, duration categories, airline groups, stop configurations). Each cluster represents a coherent segment of the search results with characteristic features, creating natural division points that help users understand and navigate the options. This segmentation transforms the unstructured list of flights into organized groups with clear distinguishing features, making the filtering process more intuitive and informative.
3Adaptability or versatility
If a large number of flight itineraries are presented, then more options are available, but it becomes difficult to quickly identify appropriate flights
Solution Approach 1:
The system introduces cluster representations as intermediary elements between the raw flight itineraries and the user's selection process. Each cluster acts as a mediator that summarizes multiple flights with similar characteristics, providing a condensed view that preserves the essential information needed for decision-making. This intermediary layer allows users to quickly identify appropriate flights by reviewing cluster characteristics rather than individually examining each flight option, maintaining comprehensive coverage while significantly improving detectability and selection speed.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing travel itinerary filters. In one aspect, a method includes receiving a flight query including a plurality of parameters; determining a plurality of itineraries that satisfy the parameters of the flight query; clustering the plurality of itineraries into a plurality of clusters, wherein the clusters depend upon values of particular features of the plurality of itineraries that satisfy the flight query, and wherein each cluster is generated to have particular values for one or more features of a plurality of features; generating one or more filters corresponding to one or more of the clusters, wherein each filter has the particular values of the one or more features identified by the corresponding cluster; and providing the plurality of itineraries that satisfy the flight query and the one or more filters for filtering the plurality of itineraries.