Language Detection via Browsing History Analysis
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Solution Overview
Problem
Current content selection methods in computer networked environments often fail to accurately identify the languages used by multilingual users, leading to limited content options and reduced user interaction quality, as they rely solely on declared language settings or short query keywords, which are ambiguous and lack context.
Innovation Solution
A system that analyzes user browsing history and search results using a language recognition model to determine candidate languages, calculating confidence scores and updating language sets based on user interactions and content relevance, thereby selecting content items in languages the user is proficient in.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content selection relies solely on declared language settings or short query keywords, then the system complexity is low, but language prediction accuracy is poor
Solution Approach 1:
The system performs preliminary analysis of user browsing history before content selection to build a comprehensive language profile. By pre-processing historical data and identifying language patterns in advance, the system achieves higher prediction accuracy without significantly increasing complexity during the actual content selection process.
Solution Approach 2:
The patent introduces an intermediary language recognition model that acts as a mediator between raw browsing history data and content selection. This model processes and interprets historical data, extracting language information that then informs content selection, thereby improving accuracy while managing system complexity through modular design.
2Measurement precision
If the system analyzes browsing history and search results to determine candidate languages, then language prediction accuracy increases, but computing and network resource consumption increases
Solution Approach 1:
The system extracts only the essential language-related features from browsing history and search results, rather than processing all available data. By selectively extracting language indicators, timestamps, and interaction patterns, the system achieves high prediction accuracy while minimizing computing and network resource consumption.
Solution Approach 2:
The system performs partial analysis by focusing on key language-determining elements in browsing history rather than comprehensively analyzing all user interactions. This selective approach provides sufficient language prediction accuracy while reducing the computational burden and resource consumption.
3Adaptability or versatility
If the system uses multiple data sources including browsing history and search results, then content relevance improves, but device complexity increases
Solution Approach 1:
The system segments the data processing task into distinct modules: browsing history analysis, search result analysis, language profile construction, and content selection. Each module handles specific data sources and processing logic independently, improving content selection adaptability while managing complexity through modular, organized processing.
Data Source
AI summary
Systems and methods of determining languages of users in networked environments are provided herein. A data processing system having one or more processors coupled with memory can receive, from a client device, a request for content identifying an account profile. The data processing system can receive a request for content identifying an account profile and including one or more keywords; determine a first set of candidate languages from a plurality of languages; determine a second set of candidate languages based on one or more information resources associated with the one or more keywords; calculate confidence scores for at least some of the second set of candidate languages; and update the first set of candidate languages based on the confidence scores for the at least some of the second set of candidate languages.