Traveler Recommendations System Using Location Filtering
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Solution Overview
Problem
Travelers face challenges in finding relevant content during trips due to the time and resources required to search for information, often missing out on valuable experiences like hotel recommendations, activities, or local information, as existing systems fail to efficiently provide tailored recommendations based on travel mode and destination.
Innovation Solution
A system that evaluates locational data to identify location point pairings indicative of air flight travel, filters out non-relevant data, and provides personalized recommendations for air flight travelers, including hotel, car rental, and activity suggestions, using historic travel data to infer future trips and tailor content based on travel reasons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users search for relevant travel content manually, then they can find information about hotels, activities, and local information, but they spend substantial time and computing resources searching
Solution Approach 1:
The system performs preliminary actions by automatically evaluating locational data, identifying location point pairings indicative of air flight travel, and generating personalized recommendations before the user needs them. This eliminates the need for users to manually search for travel content by pre-processing location data and preparing tailored recommendations for hotels, car rentals, activities, and local information.
Solution Approach 2:
The system enables self-service by automatically generating travel recommendations without requiring user initiation. The system monitors location data, identifies travel patterns, and autonomously provides personalized content recommendations, allowing users to receive relevant information passively rather than actively searching for it.
2Loss of information
If users search for relevant travel content manually, then they can find information about hotels, activities, and local information, but they may forego searching and thus miss out on experiencing such content
Solution Approach 1:
The system enables self-service by automatically generating travel recommendations without requiring user initiation. The system monitors location data, identifies travel patterns, and autonomously provides personalized content recommendations, allowing users to receive relevant information passively rather than actively searching for it.
Solution Approach 2:
The system acts as an intermediary between the user and travel information. It processes location data, identifies travel contexts, and mediates by delivering filtered, personalized recommendations that bridge the gap between available content and user needs, making information discovery effortless.
3Adaptability or versatility
If the system provides personalized recommendations based on travel mode and destination, then users receive relevant content, but the system requires evaluating locational data and filtering based on multiple criteria
Solution Approach 1:
The system applies segmentation by breaking down the complex task of recommendation generation into distinct modules: evaluating locational data, generating location point pairings, filtering based on air flight speed and distance thresholds, verifying against flight schedules, and generating personalized recommendations. This modular approach manages complexity while maintaining adaptability.
Solution Approach 2:
The system uses parameter changes by applying specific thresholds (air flight speed threshold, air flight travel distance threshold) and filtering criteria to transform raw location data into meaningful travel context. These parameter-based filters enable the system to adaptively identify air flight travel patterns and generate appropriate recommendations without requiring complex algorithms.
4Measurement precision
If the system filters location point pairings based on air flight speed threshold and distance threshold, then users can identify relevant travel data, but the system requires substantial computational resources
Solution Approach 1:
The system uses parameter changes by applying specific thresholds (air flight speed threshold, air flight travel distance threshold) and filtering criteria to transform raw location data into meaningful travel context. These parameter-based filters enable the system to adaptively identify air flight travel patterns and generate appropriate recommendations without requiring complex algorithms.
Solution Approach 2:
The system applies local quality by focusing computational resources only on location point pairings that meet specific criteria (speed and distance thresholds indicative of air flight). Rather than processing all location data uniformly, the system applies targeted filtering to identify relevant travel patterns, reducing overall computational burden while maintaining precision.
Data Source
AI summary
One or more computing devices, systems, and/or methods for providing recommendations to travelers are provided. For example, locational data associated with a device of a user is evaluated to identify a set of location points at which the device was located over time. Location point pairings are generated from the set of location points, where a location point pairing may comprise a departure location point and an arrival location point. The location point pairings are filtered based upon various criteria to remove location point pairings that are not indicative of air flight travel (e.g., a location point pairing not satisfying an air flight speed threshold or an air flight travel distance threshold). The user may be determined as an air flight traveler that has recently used air flight travel to reach a destination location. A recommendation of content for the destination location is provided to the user.