Personalized REtrieval

Personalized retrieval aims to tailor information retrieval systems to individual users, enhancing search relevance and user experience by moving beyond generic keyword matching. Current research focuses on leveraging large language models (LLMs) and embedding-based methods, often incorporating user interaction history and contextual information to generate personalized search results, sometimes through techniques like incremental user embedding modeling or retrieval-augmented summarization. This field is significant because it promises to improve the efficiency and effectiveness of information access across diverse applications, from e-commerce search to job matching and personalized news feeds, ultimately leading to more relevant and satisfying user interactions.