Keyword Ranking Model for Professional Profile Summaries
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Solution Overview
Problem
Social network members face challenges in creating effective professional summaries, which are crucial for visibility and opportunities, as they require synthesizing diverse profile information and may result in poorly constructed summaries affecting their ranking and search results.
Innovation Solution
A system that suggests keywords relevant to a member's professional experience and expertise by analyzing their profile attributes and generating a model to rank phrases, presenting the most relevant ones for inclusion in their summary to enhance visibility and attractiveness to recruiters and search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If members manually create professional summaries by synthesizing their own profile information, then they can control the content and personalization, but the quality and effectiveness of summaries deteriorate due to lack of expertise in synthesis and keyword optimization
Solution Approach 1:
The system enables members to automatically generate professional summaries by having the system extract and synthesize information from their existing profile data without requiring manual intervention. The system serves itself by using the member's own profile information as input to generate the summary, eliminating the need for members to manually synthesize their own information while maintaining high quality through automated analysis and keyword ranking algorithms.
2Loss of information
If members invest significant time and effort to craft detailed professional summaries, then the completeness and depth of information improves, but the time required and complexity of the process increases
Solution Approach 1:
The system performs preliminary actions by automatically extracting, analyzing, and ranking keywords from the member's profile information before the summary is actually needed. The system pre-processes the profile data, identifies relevant phrases and keywords, and ranks them by importance, so that when the member needs a summary, it is already prepared with optimized content rather than requiring the member to spend time crafting it manually.
3Measurement precision
If the system provides comprehensive keyword suggestions from all profile attributes, then the relevance and accuracy of keywords improves, but the complexity of the analysis and processing increases
Solution Approach 1:
The system replaces complex manual analysis and evaluation mechanisms with automated computational algorithms. Instead of requiring complex human judgment to determine keyword relevance, the system uses automated text analysis, frequency counting, and ranking algorithms to objectively evaluate and rank phrases based on their relevance to the member's profile. This substitution of mechanical/computational processes for manual analysis maintains accuracy while reducing the perceived complexity for the user.
Data Source
AI summary
A suggested keywords system is configured for identifying phrases, which are most relevant to experience and expertise of a professional network member, and which the member may be interested in weaving into their profile summary. The suggested keywords system generates a model, for each phrase, that calculates probability of that phrase being present in a profile that is characterized by the absence of certain attributes and by the presence of certain attributes. Based on the model, the suggested keywords system calculates a ranking value for the phrase for a particular target profile. The phrases with the higher rank are considered to be more relevant in describing professional background of the target member. A certain number of phrases that have the highest ranking are presented to the member as suggested keywords to be included in their professional summary.