Social Network Keyword Query Suggestions
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Solution Overview
Problem
Social networking systems face challenges in accurately extracting and interpreting keywords from posts and comments to provide relevant search suggestions, as existing methods struggle to distinguish between ambiguous terms and determine contextually appropriate topics.
Innovation Solution
The social networking system extracts keywords from posts and comments, determines associated topics based on context, and calculates topic scores to generate suggested keyword queries that are relevant to the original post or related content, using a combination of natural language processing and machine learning algorithms to improve search query suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If keyword extraction is performed from all posts and comments, then the quantity of search suggestions increases, but the precision of relevant search results decreases due to ambiguous terms
Solution Approach 1:
The system segments the keyword extraction process by identifying and separating ambiguous keywords from clear keywords. Ambiguous keywords are processed through additional context analysis and disambiguation steps, while clear keywords are directly used for search suggestions. This segmentation allows the system to maintain high quantity of suggestions while improving precision by treating different keyword types differently.
Solution Approach 2:
The patent introduces an intermediary disambiguation mechanism that acts between keyword extraction and search suggestion generation. This intermediary layer analyzes contextual information from posts, comments, and user profiles to resolve ambiguous terms before finalizing search suggestions. The intermediary process filters out irrelevant interpretations and retains only contextually appropriate keywords, thereby maintaining precision while preserving quantity.
2Measurement precision
If context analysis is performed to determine appropriate topics for ambiguous keywords, then the accuracy of search suggestions improves, but the complexity of the processing system increases
Solution Approach 1:
The system applies partial context analysis by focusing only on ambiguous keywords that require disambiguation, rather than performing full context analysis on all keywords. For clear, unambiguous keywords, the system skips the complex context analysis step and proceeds directly to search suggestion generation. This partial action approach maintains accuracy for ambiguous terms while reducing overall system complexity by avoiding unnecessary processing.
Solution Approach 2:
The patent implements local quality by applying different processing depths to different keywords based on their ambiguity level. High-ambiguity keywords receive extensive context analysis involving multiple data sources (post content, comments, user profiles), while low-ambiguity keywords receive minimal or no context analysis. This localized quality adjustment optimizes the balance between accuracy and complexity by concentrating computational resources where they are most needed.
3Measurement precision
If multiple keywords are extracted and analyzed for each post, then the relevance of search results improves, but the time required for processing increases
Solution Approach 1:
The system performs partial keyword analysis by extracting and fully analyzing only the most significant ambiguous keywords for each post, rather than exhaustively analyzing all possible keywords. The system identifies a limited set of high-impact ambiguous terms that most affect search relevance and focuses context analysis resources on these select keywords. This partial action approach maintains high relevance by addressing critical ambiguity while reducing processing time by avoiding analysis of less important terms.
4Reliability
If verification is performed to ensure suggested queries retrieve relevant posts, then the reliability of search suggestions improves, but the productivity of the system decreases
Solution Approach 1:
The system implements partial verification by selectively verifying only the most critical search suggestions rather than performing exhaustive verification on all generated suggestions. The verification process focuses on ambiguous keywords and high-stakes search terms where reliability is most important, while skipping verification for clear, unambiguous keywords with high confidence scores. This partial verification approach maintains reliability for critical suggestions while preserving productivity by avoiding time-consuming verification of low-risk suggestions.
Data Source
AI summary
In one embodiment, a method includes accessing a post of an online social network, extracting keywords from the content of the first post and the metadata associated with the first post, determining topics associated with each extracted keyword, calculating a topic-score based on a relevance of the topic to the post for each topic, generating a suggested keyword query corresponding to the post, the suggested keyword query comprising extracted keywords corresponding to each topic having a topic-score greater than a threshold topic-score, and sending the post and the suggested keyword query to a client device of a first user for display.