Server System Classifying Web Pages Using Historical Search Queries
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Solution Overview
Problem
Current search engines face challenges in accurately identifying and serving information that matches individual users' dynamic interests due to varying preferences and the lack of classification data for web pages, leading to suboptimal search results.
Innovation Solution
A server system generates classification data for unclassified information items by leveraging historical query information and classification data of already classified items, enabling customized services by accessing and processing historical query data, user profiles, and information classification databases to provide personalized search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a search engine includes more web pages in search results to cover diverse user interests, then the quantity of information provided increases, but the relevance and quality of search results deteriorates due to lack of classification data
Solution Approach 1:
The system performs preliminary classification of web pages in advance by analyzing historical query information and user interactions. Classification data is generated and stored before search queries are executed, enabling the search engine to quickly filter and rank pages based on pre-computed classification labels without sacrificing relevance when expanding result quantity
Solution Approach 2:
The system enables web pages to self-classify by automatically analyzing their content and historical query associations. The classification process uses the web page's own interaction data with users to generate classification labels, eliminating the need for manual classification and enabling scalable classification of large numbers of pages while maintaining relevance
2Measurement precision
If the search engine attempts to accurately identify individual user interests through analysis, then the precision of information matching improves, but the complexity of the system increases due to need for dynamic profiling
Solution Approach 1:
The system introduces classification data as an intermediary layer between user queries and web page results. Instead of directly analyzing complex user behavior patterns, the system uses pre-generated classification labels of web pages as mediators to match user interests with relevant content, simplifying the profiling process while maintaining precision
Solution Approach 2:
The system changes the parameter representation from complex behavioral analysis to simple classification labels. By transforming user interests and web page characteristics into discrete classification categories, the system achieves precise matching with reduced computational complexity, enabling efficient dynamic profiling through parameter transformation
3Measurement precision
If the search engine uses manual classification methods for web pages, then the accuracy of classification data improves, but the productivity and scalability of the system deteriorates due to time-consuming manual processes
Solution Approach 1:
The system enables web pages to self-classify by automatically analyzing their own content and historical query associations. This self-service classification approach eliminates manual intervention while maintaining accuracy through objective analysis of actual user interaction data, achieving both high precision and scalability simultaneously
Solution Approach 2:
The system uses historical query information and user interaction feedback to automatically generate and refine classification data. By continuously incorporating feedback from actual search behavior, the system maintains high classification accuracy while operating at automated speeds, resolving the trade-off between manual accuracy and automated productivity
Data Source
AI summary
A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.