Travel Recommendation Engine Using Email-Derived User Profiles
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Solution Overview
Problem
Conventional travel recommendation systems rely solely on user input data and lack the ability to analyze holistic user profiles, failing to provide personalized recommendations based on comprehensive travel preferences and historical data scattered across multiple service providers.
Innovation Solution
A system that parses electronic communication data, including emails and other digital communications, to identify travel-related information, filters vendor services, and applies machine learning models to generate personalized travel recommendations by creating a user profile based on historical travel data and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional travel recommendation systems rely solely on user input data, then the system operation is simple, but the recommendation accuracy and personalization are insufficient
Solution Approach 1:
The patent merges multiple data sources including electronic communication data, vendor service information, and historical travel information into a unified user profile. This combination allows the system to leverage diverse data types (emails, texts, booking confirmations) to enhance recommendation accuracy without requiring complex manual data entry from users.
Solution Approach 2:
The patent introduces a travel recommendation system as an intermediary that automatically parses and processes electronic communication data. This intermediary component bridges the gap between raw scattered data and actionable recommendations, handling the complexity of data integration and analysis while presenting simplified results to users.
2Adaptability or versatility
If travel details are scattered among several service providers, then each provider can maintain simple independent operations, but the holistic view of user preferences is lost
Solution Approach 1:
The patent creates a universal user profile that can accommodate multiple data sources and types of travel information. The system is designed to handle various electronic communications (emails, texts, messages) and integrate them into a single comprehensive profile, making it adaptable to different service providers and data formats without requiring separate systems for each.
Solution Approach 2:
The patent segments the data integration process into distinct modules: parsing electronic communications, extracting travel details, filtering vendor services information, and generating user profiles. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while achieving holistic data integration.
3Loss of information
If conventional services require explicit user input for preferences, then data collection is simple and privacy-preserving, but the system lacks access to implicit travel interests
Solution Approach 1:
The patent performs preliminary parsing and analysis of electronic communication data to extract travel-related information before the user explicitly requests recommendations. By proactively processing emails, texts, and other communications to identify travel details (destinations, dates, preferences), the system captures implicit user interests that would otherwise remain undetected.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated electronic communication parsing. Instead of requiring users to explicitly enter preferences, the system uses automated text analysis and pattern recognition to extract travel information from electronic communications, substituting mechanical data entry with automated information extraction.
Data Source
AI summary
Disclosed are systems and methods for generating recommendations to users based on historical travel information and electronic communication data. The disclosed systems and methods provide a novel framework for automating the transmission of electronic travel-related recommendations to users by consistently monitoring electronic messages received at an electronic communication mailbox corresponding to a user. The disclosed framework operates by leveraging historical user data, data parsed from electronic communication mailbox corresponding to a user, or various vendor information, and using the aforementioned data as inputs for travel-related recommendation models, in order to generate and transmit the optimal travel-related recommendations to a user.