Beyond Keywords: Understanding User Intent for Personalized Financial Search

Traditional financial information retrieval systems primarily rely on market data feeds and predefined search options.While these systems offer a diverse overview of financial information, traditional search engines may not always fully understand the specific information needs behind a user's search.This study explores an elegant approach that powers accurate user query analysis to personalize search results and cater to the exact information goals of financial users.The proposed model analyses user queries to understand the intent behind a search.This allows users to get results and recommendations, providing a more nuanced and user-centric experience than traditional approaches.The researchers discuss this approach's potential benefits, including improved user experience, understanding of user intent, and the ability to anticipate user needs.This study also listed the challenges associated with user data collection and privacy concerns.This research contributes to developing comprehensive, user-centric, and personalized next-generation financial information retrieval systems.