Content Marking Search Recommendations for Faster Query Discovery
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Solution Overview
Problem
Current information search methods rely heavily on manual input of search keywords, leading to inefficiencies in search accuracy and time consumption, as users often need to input multiple keywords to find relevant information, and there is a lack of direct integration of user marking behavior for intelligent search recommendations.
Innovation Solution
An information search method and apparatus that allows users to mark content segments directly in a content detail interface, generating intelligent search recommendations based on segment detail information and previous marking operations, using natural language processing and machine learning to provide recommended search information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually input search keywords, then search results can be obtained, but search efficiency is low and time consumption is high
Solution Approach 1:
The system automatically generates search recommendations based on user marking behaviors without requiring manual keyword input. The search system serves itself by inferring user intentions from marking patterns, eliminating the need for users to manually formulate search queries and thus improving search efficiency while reducing time consumption.
Solution Approach 2:
The system performs preliminary analysis of user marking behaviors and generates search recommendations in advance before users need to conduct searches. By pre-processing marking data and generating relevant search queries beforehand, the system enables faster information retrieval when users actually need to search, reducing the time they would otherwise spend formulating and executing multiple search queries.
2Measurement precision
If users input multiple search keywords to improve accuracy, then search result accuracy improves, but search time increases
Solution Approach 1:
The system continuously monitors and analyzes user marking behaviors to generate personalized search recommendations. By using feedback from user marking patterns, the system can accurately infer user intentions and provide precise search results without requiring users to manually input multiple keywords, thus maintaining high search result accuracy while reducing search time.
Solution Approach 2:
The system replaces the mechanical process of manual keyword input with an automated intelligent recommendation system. Instead of requiring users to mechanically type and refine multiple search keywords, the system uses AI-based analysis of marking behaviors to automatically generate accurate search queries, substituting manual mechanical input with automated intelligent processing.
3Productivity
If the search system relies on manual keyword input, then implementation is simple, but search efficiency is low
Solution Approach 1:
The search system performs self-service by automatically analyzing user marking behaviors and generating search recommendations without requiring complex manual configuration. The system serves itself by autonomously inferring user intentions from marking patterns, improving search efficiency while keeping the user interface simple and avoiding the need for users to manually configure complex search parameters.
Solution Approach 2:
The system introduces an intermediary layer between users and the search engine that automatically processes marking behaviors and generates search queries. This intermediary component simplifies the overall system by hiding the complexity of behavior analysis and query generation from users, presenting a simple marking interface while automatically handling the complex search recommendation generation in the background.
4Loss of information
If users perform multiple searches to find satisfying information, then information completeness improves, but time consumption increases
Solution Approach 1:
The system performs preliminary generation of comprehensive search recommendations based on user marking behaviors before users need to retrieve information. By pre-analyzing marking patterns and generating multiple relevant search queries in advance, the system enables users to access complete information more quickly without needing to perform multiple sequential searches, thus improving information completeness while reducing time consumption.
Solution Approach 2:
The system uses feedback from user marking behaviors to generate comprehensive and personalized search recommendations. By continuously analyzing how users mark content, the system can infer complete information needs and provide comprehensive search results in a single operation, eliminating the need for users to perform multiple searches to gather complete information.
Data Source
AI summary
An information search method, apparatus, and computer-readable storage medium for providing intelligent search recommendations based on content marking behavior. The method displays a content detail interface and receives marking operations selecting content segments within target content. Marking identifiers are displayed for the selected segments. Based on segment detail information from current and previous marking operations, recommended search information is generated and displayed. Users can select target recommended search information through search operations, and corresponding search results are displayed. This approach enables personalized search suggestions derived from user marking patterns and content interaction history, improving search relevance and user experience.