What effect does rebuilding the model have on relevance ranks?

Rebuilding the model effectively updates the relevance ranks based on the latest document data available. Over time, documents may have changes in their content, importance, or user interactions that could influence how relevant they are to specific search queries. When a model is rebuilt, it incorporates this updated information, ensuring that the relevance ranks reflect current conditions and are more aligned with user needs.

This process is crucial in analytics and search functionalities, as it helps maintain accuracy and relevance within user searches. By applying new data insights, the model can adjust how documents are ranked, thus improving the likelihood that users will find the most pertinent information based on their search criteria. This responsiveness to fresh data keeps the system effective and user-friendly, directly supporting a dynamic search experience.