Search Result

Search result diversification aims to present users with a broader range of relevant information, combating the limitations of solely prioritizing short-term engagement or relying on simplistic ranking algorithms. Current research focuses on incorporating user intent, employing multi-agent reinforcement learning to optimize diversity metrics, and leveraging large language models to dynamically generate and organize diverse search results, including different media types. These advancements improve user experience by facilitating exploration of diverse viewpoints and content, impacting both the design of search engines and the effectiveness of online advertising.