Search Behavior

Search behavior research aims to understand how users interact with information retrieval systems, ultimately improving search engine effectiveness and user experience. Current research focuses on leveraging large language models (LLMs) to simulate user behavior, enhance relevance modeling by incorporating user interaction data, and improve query understanding through techniques like context-aware query rewriting. These advancements are significant because they enable more accurate modeling of complex search tasks, leading to better personalized search results and more efficient information access across various domains, including e-commerce and scientific research.