Candidate Generation

Candidate generation is a crucial preprocessing step in many information retrieval and machine learning tasks, aiming to efficiently identify a subset of the most relevant candidates from a vast search space before more computationally expensive ranking or classification stages. Current research focuses on improving the efficiency and accuracy of candidate generation across diverse applications, employing techniques ranging from information retrieval methods and transformer-based neural networks to hybrid approaches combining lookup and dense retrieval. These advancements are driving improvements in various fields, including recommendation systems, entity linking, and robotic manipulation, by enabling faster and more accurate processing of large datasets and complex queries.