Relevance Modeling

Relevance modeling aims to identify the items most pertinent to a user's query or need, a crucial task across diverse fields like information retrieval, human-robot collaboration, and e-commerce. Current research emphasizes improving relevance estimation using large language models (LLMs) and incorporating user behavior data, often within two-tower architectures or through prompt engineering techniques, to enhance accuracy and robustness. These advancements have significant implications for improving search engine performance, optimizing human-computer interaction, and enabling more efficient and safer autonomous systems.