Millist reankerit kasutati?

Re-ranking tools are used to improve the quality of result ranking, for example in search engines or recommendation systems. In my practice, I have used the following approaches and tools:

• Using gradient boosting models (e.g., XGBoost, LightGBM) for re-ranking candidates.
• Applying neural networks, such as BERT or other transformers, to evaluate relevance in the second stage.
• Frameworks: scikit-learn for classical models, TensorFlow or PyTorch for deep learning.

Example: initially, a basic ranker provides the top-N results, then a LightGBM-based reranker reevaluates them considering additional features to improve accuracy.