Personalized Question Answer Database Using Morphological Analysis
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Solution Overview
Problem
Existing question-answering databases face challenges in personalizing articles based on individual user interests, as conventional methods only prioritize keywords by frequency and do not support personalized article recommendations.
Innovation Solution
A system and method that utilize machine learning and natural language processing to extract profile keywords from user profiles, assign personalization scores to question-answer articles based on morphological and dependency analysis, and output relevant data based on these scores to personalize article recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional keyword prioritization by frequency is used, then the system is simple to implement, but it cannot support personalized article recommendations
Solution Approach 1:
The patent transforms the keyword prioritization mechanism from simple frequency counting to a scoring system that incorporates multiple parameters including user profile matching, article relevance, and engagement metrics. This parameter expansion enables personalization while maintaining a structured approach to complexity management.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that bridges user profiles and article content. This mediator computes personalization scores by evaluating the alignment between user characteristics and article attributes, enabling personalized recommendations without direct complex user-article matching.
2Measurement precision
If morphological and dependency analysis is applied to question-answer articles, then personalization accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies morphological and dependency analysis in advance during the article processing stage, creating structured representations and extracting key features before they are needed for personalization scoring. This preliminary processing reduces the computational burden during real-time recommendation generation.
Solution Approach 2:
The patent divides the article processing into distinct segments: morphological analysis of individual words, dependency analysis of phrase relationships, and scoring computation. This segmentation allows each analysis type to be optimized independently and enables parallel processing to reduce overall processing time.
Data Source
AI summary
A computer-implemented method for constructing and searching structured data of question sentences in a question-answer database using machine learning and natural language processing includes receiving one or more question-answer articles as input from one or more question-answer databases over at least one network, extracting one or more profile keywords from a profile associated with a user, assigning a personalization score corresponding to the user to each of the one or more question-answer articles based on the one or more profile keywords, including applying a morphological and dependency analysis to a body section of the question-answer article, receiving a query for obtaining question-answer data relevant to the user, and outputting question-answer data based at least in part on the personalization score of each question-answer article in satisfaction of the query.