Travel Recommendation Engine Using Email-Derived User Profiles
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Solution Overview
Problem
Conventional travel recommendation systems rely primarily 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 from multiple service providers.
Innovation Solution
A system that parses electronic communication data, such as emails and social media messages, to identify travel-related information, filters vendor services, and generates user profiles using machine learning models to provide personalized travel recommendations by correlating candidate offers with relevancy scores.
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 combines multiple data sources including electronic communication data, vendor service information, and historical travel information into a unified recommendation system. This merging of diverse data streams enables comprehensive user profiling and significantly improves recommendation accuracy by considering holistic user preferences rather than relying solely on direct user input.
Solution Approach 2:
The system introduces machine learning models as intermediaries that process and analyze the collected data from multiple sources. These models act as mediators between raw data and final recommendations, automatically extracting user preferences and behaviors to generate personalized recommendations without requiring complex manual processing.
2Adaptability or versatility
If the system parses electronic communication data and uses machine learning models, then the recommendation personalization is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user profile information, travel preferences, and behavioral patterns in advance. This preparation work is done before actual recommendation generation, allowing the system to quickly retrieve and match pre-analyzed user data with available travel offers, thereby reducing real-time processing time while maintaining high personalization capability.
3Loss of information
If the system collects data from multiple service providers, then the holistic user profile is improved, but the information integration complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple types of data sources including electronic communications, vendor services, and historical travel information through a single integrated system. This multi-functional approach allows the system to uniformly process diverse data formats and structures, reducing integration complexity while achieving comprehensive user information collection.
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.