Search Evaluation System Using Skill Ratings to Detect Missing Content
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Solution Overview
Problem
Current searchable content systems fail to account for the possibility of missing knowledge in their databases, which can be identified by user search skills, leading to inefficiencies in customer support and resource allocation.
Innovation Solution
A method and system that utilize user skill ratings to update cumulative scores for search strings and databases, identifying missing content by comparing these scores to thresholds, thereby weighting the significance of failed searches based on the searcher's skill level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If searchable content is maintained in a knowledgebase for customer support, then user support efficiency is improved, but the system cannot distinguish between failed searches due to missing content versus searcher skill limitations
Solution Approach 1:
The system implements feedback by tracking searcher skill ratings and using them to evaluate search outcomes. When a searcher with a known skill rating performs a search, the system uses this feedback to determine whether the search failure indicates missing content or skill-related issues, thereby improving the identification of actual knowledge gaps in the knowledgebase.
Solution Approach 2:
The system changes the parameter of search evaluation by incorporating searcher skill ratings as a weighting factor. Instead of treating all search failures equally, the system adjusts the significance of each failed search based on the searcher's skill level, allowing for more accurate identification of missing content versus skill-related failures.
2Measurement precision
If all search failures are treated equally to identify missing content, then content gaps are detected, but skill-related search failures create false positives indicating missing content
Solution Approach 1:
The system changes the evaluation parameter by introducing searcher skill ratings as a weighting factor. High-skill searchers' failed searches are given more weight in identifying missing content, while low-skill searchers' failures are weighted less, reducing false positives and improving the reliability of content gap identification.
Solution Approach 2:
The system applies local quality by treating different searchers differently based on their individual skill ratings. Each searcher's failed searches are evaluated with a weight corresponding to their skill level, allowing the system to locally adjust the significance of each search failure rather than applying a uniform evaluation standard.
3Measurement precision
If searcher skill ratings are incorporated into search evaluation, then accurate identification of missing content is achieved, but system complexity increases due to skill rating determination and score updating
Solution Approach 1:
The system performs preliminary action by determining searcher skill ratings in advance, before the search takes place. This pre-established skill rating information is then used to weight search outcomes, simplifying the overall process by avoiding complex real-time analysis of searcher competence during the search evaluation phase.
Solution Approach 2:
The system implements self-service by automatically determining searcher skill ratings and using them to weight search outcomes without requiring manual intervention. The system autonomously evaluates whether a search failure indicates missing content or skill-related issues, reducing the need for manual content gap analysis.
Data Source
AI summary
An approach for identifying missing content is provided. An approach includes: receiving a search string; determining a skill rating associated with a searcher that generated the search string; and searching a database using the search string. The approach also includes: updating at least one of a first score associated with the search string and a second score associated with the database based on the skill rating; and generating an alert based on one of the first score exceeding a first threshold and the second score exceeding a second threshold.