Re-Ranking

Re-ranking involves taking an initially retrieved set of results from a search engine or database and sorting them again using additional algorithms or criteria. This secondary sorting is often applied to enhance relevance, accuracy, or user satisfaction. Common methods include using machine learning models that consider context, user behavior data, or specific content features not captured in the initial retrieval phase. For instance, Google's search algorithms frequently apply re-ranking processes based on real-time feedback and user interaction metrics.