User Interest Profile Generation Using Knowledge Graph Tag Matching

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Solution Overview

Problem

Existing methods for generating user interest profiles require significant manual intervention, including data tagging and rule formulation, leading to high labor costs and inefficiencies.

Innovation Solution

A method that extracts keywords from user input information, matches them with tags in a knowledge graph, and sorts these tags to generate a user interest profile without manual intervention, utilizing automated processes to derive interest tags from an encyclopedia knowledge base.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual data tagging and rule formulation are used to generate user interest profiles, then the accuracy and reliability of profiles can be maintained through human expertise, but labor costs increase and processing efficiency decreases

Engineering Contradiction:

Improveprofile accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs self-service by automatically extracting keywords from user input, matching them with knowledge graph tags, and generating interest profiles without requiring manual tagging or rule formulation. The knowledge graph itself serves as the automated decision-making mechanism, eliminating human intervention while maintaining reliability through structured data relationships.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual data tagging and rule formulation with an automated information extraction and matching system. Natural language processing algorithms substitute human analysts, while automated keyword-tag matching replaces manual rule application, thereby improving productivity without sacrificing profile accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual data tagging and rule formulation are used to generate user interest profiles, then the quality and reliability of profiles can be maintained, but labor costs increase

Engineering Contradiction:

Improveprofile accuracyVSAvoidlabor cost

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

3Productivity

If automated keyword extraction and knowledge graph matching are used to generate user interest profiles, then processing efficiency and productivity improve, but the system complexity increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary action by pre-building a comprehensive knowledge graph with organized tags and relationships before runtime. This pre-processing step stores structured knowledge about entities, attributes, and relationships, allowing the automated extraction and matching processes to operate efficiently without complex real-time computations, thereby managing system complexity while maintaining high productivity.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If automated keyword extraction and knowledge graph matching are used to generate user interest profiles, then processing efficiency improves, but the difficulty of detecting and measuring accurate matches increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidmatching accuracy

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system implements feedback mechanisms by evaluating the quality of keyword-tag matches and using this information to refine future matching operations. The knowledge graph's structured relationships provide inherent feedback about the correctness of matches, allowing the system to detect and measure matching accuracy through the consistency and coherence of the generated interest profiles against known user behaviors and preferences.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12596737B2Method for generating user interest profile, electronic device and storage medium

Publication Date: 2026.04.07 BAIDU INT TECH (SHENZHEN) CO LTD

AI summary

A method for generating a user interest profile includes: generating at least one keyword by extracting information from input information of a user; generating interest tags corresponding to the at least one keyword by matching the at least one keyword with tags corresponding to nodes of a knowledge graph; sorting the interest tags corresponding to the at least one keyword; and generating a user interest profile based on the sorted interest tags corresponding to the at least one keyword.