A process where user feedback is used to refine search results, improving the accuracy and relevance of retrieved information.
How It Works
Relevance feedback involves collecting user feedback on search results and using this information to adjust the retrieval process. This iterative approach helps in refining search algorithms to better match user intent.
Technical Details
Feedback can be explicit, where users rate results, or implicit, inferred from user interactions. Algorithms use this feedback to adjust ranking models, often employing machine learning techniques to learn from user behavior.
Best Practices
Implement both explicit and implicit feedback mechanisms