Trip Plan Builder Using Social Media Data Mining
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Solution Overview
Problem
Travelers face challenges in planning trips as they struggle to recall and share details of past experiences, and existing technologies lack effective methods to leverage collective knowledge from social media for personalized trip planning.
Innovation Solution
A method involving processors that receive trip information from social media, calculate weighted ranks for stopover points, and create customized trip plans based on traveler preferences, using data mining and navigation systems to build and recreate trip plans dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If travelers manually recall and document past trip experiences, then personal trip memories are preserved, but time consumption and effort increase significantly
Solution Approach 1:
The system automatically captures trip data from social media sources without requiring manual intervention from travelers. The trip reconstruction system autonomously retrieves photos, check-ins, and social media posts to build trip itineraries, eliminating the need for travelers to manually document their experiences while preserving comprehensive trip information.
Solution Approach 2:
The system creates digital copies of trip experiences by retrieving and reconstructing data from existing social media sources. Instead of requiring original manual documentation, the system copies relevant trip information from social media platforms, photos, and check-ins to recreate accurate trip itineraries automatically.
2Ease of operation
If generic trip planning information is provided to all travelers, then planning simplicity is maintained, but personalization and relevance to individual preferences are lost
Solution Approach 1:
The system applies different levels of personalization to different aspects of trip planning. It retrieves and weights stopover points based on individual traveler preferences, social media activity, and historical data, while maintaining overall system simplicity. Each traveler receives customized stopover recommendations tailored to their specific interests and past behavior patterns.
Solution Approach 2:
The system pre-calculates and ranks potential stopover points based on traveler preferences and historical data before the traveler needs to make decisions. By performing this analysis in advance, the system prepares personalized recommendations that are ready when needed, maintaining ease of operation while delivering highly adapted results.
3Measurement precision
If comprehensive trip data from multiple social media sources is collected, then trip reconstruction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system introduces a trip reconstruction engine as an intermediary layer between multiple social media data sources and the final trip itinerary. This intermediary component standardizes and integrates data from diverse sources including Facebook, Instagram, and Google Photos, managing the complexity of multi-source data collection while improving reconstruction accuracy through unified processing.
Solution Approach 2:
The system dynamically adjusts the weighting parameters of different data sources based on their relevance and reliability. By changing the parameters that determine how much weight is given to each social media source, the system optimizes trip reconstruction accuracy while managing processing complexity through adaptive parameter adjustment rather than fixed complex rules.
4Manufacturing precision
If weighted ranking algorithms are used to prioritize stopover points, then trip plan quality is improved, but computational requirements and processing time increase
Solution Approach 1:
The system applies weighted ranking to the most relevant stopover points rather than calculating rankings for all possible locations. By focusing computational effort on a subset of high-priority candidates identified through initial filtering based on traveler preferences and social media activity, the system achieves high trip plan quality while reducing overall computational energy requirements.
Data Source
AI summary
A method and system for building a trip plan from various sources is provided. The method includes receiving information detailing a planned trip from a social media website. A weighted rank is calculated for one or more stopover points associated with the planned trip. A route for the planned trip, customized for the current travelers, is created, based on the calculated weighted rank of the stopover points and on a selection from the current travelers.