Keyword Prioritization via Probabilistic Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing on-line social network systems face challenges in effectively prioritizing keywords for job search results, as current methods rely solely on the number of job postings containing a keyword, which can lead to over-utilized keywords like 'assistant' being prioritized unnecessarily, affecting the relevance and quality of search results.
Innovation Solution
Implementing a search engine optimization (SEO) system that calculates priority scores for keywords based on their popularity and relevance, using a probabilistic model that considers the likelihood of a keyword being included in a search query and producing relevant results, and incorporating user engagement signals and profile data to generate accurate priority scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword prioritization is based solely on the number of job postings containing the keyword, then implementation simplicity is maintained, but search result relevance deteriorates due to over-utilized keywords being prioritized unnecessarily
Solution Approach 1:
The patent changes the parameters used for keyword prioritization from a single metric (number of job postings) to multiple metrics including popularity score, relevance score, and priority score. This allows the system to maintain implementation simplicity while improving search result relevance by considering multiple factors in the prioritization process.
Solution Approach 2:
The patent creates a composite scoring system that combines multiple scores (popularity score, relevance score, and priority score) to evaluate keywords. This composite approach allows the system to balance simplicity with accuracy by integrating multiple evaluation dimensions into a unified prioritization framework.
2Measurement precision
If a probabilistic model with multiple scores is used for keyword prioritization, then search result relevance is improved, but system complexity increases
Solution Approach 1:
The patent segments the keyword prioritization process into distinct components: popularity score calculation, relevance score calculation, and priority score generation. Each component handles a specific aspect of the evaluation, making the overall complex system more manageable and easier to implement by dividing it into independent, focused modules.
Solution Approach 2:
The patent introduces an intermediary priority score that synthesizes the popularity score and relevance score. This intermediary metric acts as a mediator that combines multiple evaluation criteria into a single prioritization value, simplifying the decision-making process while maintaining high accuracy in keyword ranking.
3Productivity
If popular keywords are prioritized based on frequency alone, then processing efficiency is maintained, but information quality deteriorates due to inclusion of over-utilized keywords
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors search queries and user interactions to calculate popularity scores and relevance scores for keywords. This feedback loop allows the system to continuously refine keyword prioritization based on actual usage patterns, maintaining processing efficiency while improving information quality by identifying and prioritizing truly valuable keywords.
Data Source
AI summary
A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.