First Stage Ranker

First-stage rankers are crucial components of information retrieval systems, aiming to efficiently select the most relevant items from a vast pool of candidates before further refinement. Current research emphasizes improving the accuracy and efficiency of these rankers, focusing on techniques like transformer-based models, prompt engineering for zero-shot learning, and multi-task learning to incorporate diverse signals. These advancements are significant because they directly impact the user experience in applications ranging from e-commerce search to recommender systems, improving both relevance and fairness of results.