Search Result Ranking Adjustment for User Skill Adaptation
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Solution Overview
Problem
Search users with varying skill levels face challenges in finding relevant documents within pre-configured repositories, as there is no effective closed-loop feedback system to improve their searching skills, and less skilled users may avoid technical documents despite their relevance, leading to a lack of identification of needed documentation.
Innovation Solution
A computer-based system that adjusts search result rankings based on user skill ratings, using feedback and monitoring to enhance the relevance of search results and improve user skills by incorporating the expertise of more skilled searchers within the same technical category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search results are ranked using standard relevance algorithms, then search speed and basic functionality are maintained, but less skilled users cannot find relevant technical documents and their skill level does not improve
Solution Approach 1:
The search system dynamically adapts results based on user skill level. Less skilled users receive results adjusted to match their current understanding, while more skilled users receive more advanced results. This dynamic adjustment allows the system to serve users at different skill levels effectively while providing a pathway for skill development through progressive exposure to more complex content.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with search results (selections, views, time spent) are monitored and used to update skill ratings. This feedback loop enables the system to progressively improve result relevance for each user as their skill level increases, transforming static search into an adaptive learning system.
2Loss of information
If advanced technical documents are made more visible, then information completeness is improved, but less skilled users may be overwhelmed and avoid using them
Solution Approach 1:
The system applies different quality levels of document presentation to different user skill levels. Less skilled users receive simplified or contextualized versions of technical content, while more skilled users access the full technical depth. This local quality adjustment ensures that all users can access and understand the information appropriate to their level without losing completeness for those ready for it.
Solution Approach 2:
The system performs preliminary actions by pre-adjusting search results based on user skill level before presentation. Documents are pre-filtered, pre-ranked, or pre-annotated according to the user's demonstrated capabilities, so that less skilled users are not overwhelmed by raw technical content while still having access to it when ready.
3Device complexity
If no feedback system is implemented, then system simplicity is maintained, but user skill improvement and documentation gap identification cannot occur
Solution Approach 1:
The system implements feedback mechanisms where user interactions with search results (selections, views, time spent) are monitored and used to update skill ratings. This feedback loop enables the system to progressively improve result relevance for each user as their skill level increases, transforming static search into an adaptive learning system.
4Measurement precision
If search results are customized for individual skill levels, then result relevance is improved, but computational complexity increases
Solution Approach 1:
The system changes key parameters including user skill level ratings, document difficulty classifications, and result ranking weights to customize search outcomes. By adjusting these parameters based on user profiles and interaction history, the system achieves personalized relevance without requiring completely separate search algorithms for each user level.
Data Source
AI summary
An approach for adjusting ranked search results based on user data is provided. An approach includes: receiving a search query from a search user; generating a ranked result set based on the search query; generating an adjusted ranked result set by adjusting the ranked result set based on a skill rating of the search user; and providing the adjusted ranked result set to the search user.