Query-Based User Classification for Privacy-Aware Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional user modeling techniques rely on computationally heavy data collection and lack specificity, raising privacy concerns and failing to provide granular insights into user behavior and preferences.
Innovation Solution
Query-based classification techniques that generate user classifications based on semantic properties of user queries, leveraging machine learning models to determine user characteristics such as proficiency levels, allowing for personalized responses and interface adjustments without extensive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques collect and analyze user demographic data to model user preferences, then user behavior modeling capability is improved, but computational resource expenditure increases significantly and user privacy concerns arise
Solution Approach 1:
The patent extracts only the necessary semantic information from user queries to determine proficiency levels, rather than collecting comprehensive demographic data. The system processes only the query text itself to extract features like topic keywords, question complexity indicators, and domain-specific terminology, thereby achieving user behavior modeling with minimal data collection and computational overhead.
Solution Approach 2:
The patent introduces an intermediary classification system that processes user queries through a multi-stage pipeline: initial filtering to identify query type, followed by feature extraction to derive proficiency indicators, and finally classification to determine user characteristics. This intermediary processing layer enables accurate user modeling without requiring direct access to or extensive processing of sensitive user data.
2Measurement precision
If conventional techniques collect comprehensive user demographic data, then user categorization capability is improved, but user privacy protection deteriorates
Solution Approach 1:
The system extracts only the minimal necessary information from user interactions—specifically, semantic features from query text such as topic domain, complexity level, and terminology usage. This extraction approach enables effective user categorization while completely avoiding collection of sensitive demographic data like age, gender, location, or personal identifiers, thereby protecting user privacy.
Solution Approach 2:
The system enables users to effectively categorize themselves through their own query behavior. By analyzing the semantic properties of queries that users voluntarily provide, the system infers proficiency levels and user characteristics without requiring users to disclose personal information or explicitly provide demographic data, thus maintaining privacy while achieving accurate categorization.
3Ease of manufacture
If conventional techniques use generalized user categories, then implementation simplicity is improved, but content-specific decision-making accuracy deteriorates
Solution Approach 1:
The patent applies local quality by determining user proficiency levels separately for different topic domains rather than using a single generalized category. The system analyzes query semantics to identify the specific topic area and assesses user knowledge in that particular domain, enabling content-specific decision accuracy while maintaining implementation simplicity through modular classification processes that can be applied independently to each query.
Solution Approach 2:
The system changes the parameter of user classification from static demographic attributes to dynamic proficiency indicators based on query analysis. By extracting features such as topic relevance, question complexity, and domain terminology from each query, the system adapts user categorization to the specific content context, thereby improving content-specific decision accuracy without significantly complicating the implementation through standardized feature extraction and classification procedures.
Data Source
AI summary
Techniques for query based classification are described that support generation of classifications based on user proffered information. In an example, a processing device receives a query for processing by a machine learning model. The processing device then generates a user classification based on the query. The user classification, for instance, indicates one or more user characteristics. The processing device generates a prompt for processing by the machine learning model that includes the query and the user classification. The processing device then presents a result of the processing of the prompt by the machine learning model. For instance, the machine learning model generates a response to the query tailored to the classification. In this way, the techniques described herein support generation of content specific user classifications to perform various functionality.