Prediction Model Generating Apparatus for Travel Suitability
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Solution Overview
Problem
Current travel recommendation technologies lack the ability to effectively predict travel suitability based on traveler preferences and guide information, relying on user input and location data, which limits personalized recommendations.
Innovation Solution
A prediction model generating apparatus that classifies and scores data into variable groups using a word-level evaluation reference table, generating a prediction model through machine learning to predict travel suitability by associating traveler data with travel guide data, enabling personalized recommendations without explicit user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current travel recommendation technologies use user input and location data, then basic recommendations can be provided, but the ability to predict travel suitability based on traveler preferences and guide information is limited
Solution Approach 1:
The patent segments data into multiple variable groups (traveler variables, guide variables, travel detail variables) and processes each group separately through classification and scoring operations before combining them for prediction, thereby improving prediction accuracy while managing system complexity through modular processing
Solution Approach 2:
The patent performs preliminary classification and scoring of data into variable groups before the actual prediction process. The variable group classifying unit classifies data and the variable scoring unit scores each group, preparing processed data in advance that improves prediction accuracy without overwhelming the system during runtime
2Adaptability or versatility
If data is classified into multiple variable groups and scored by associating with other groups, then personalized recommendations can be generated, but the processing complexity increases
Solution Approach 1:
The patent divides data into distinct variable groups (traveler variables, guide variables, travel detail variables) that can be independently classified and scored, enabling personalized recommendations through targeted processing of each group while keeping the overall system manageable through clear separation of concerns
Solution Approach 2:
The variable scoring unit performs multiple functions by associating data from one variable group with another group and generating scores that reflect relationships between different data types, thereby achieving versatile personalized recommendations through a unified scoring mechanism that handles multiple variable group interactions
Data Source
AI summary
In a prediction model generating apparatus 1, data obtaining unit 11 obtains text data; variable group classifying unit 12 classifies the data into a plurality of variable groups; variable scoring unit 13 scores the data of at least one of the plurality of variable groups by associating that data with the data of another group; variable input unit 14 takes the data of the scored group as a response variable, and the data of the other group associated with the scored group as an explaining variable, and inputs those data to machine learning unit 15. The machine learning unit 15 generates, through machine learning, a prediction model predicting the response variable from the explaining variable.