MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification

Understand

Search result diversification (SRD), which aims to ensure that documents in a ranking list cover a broad range of subtopics, is a significant and widely studied problem in Information Retrieval and Web Search.

• Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the highest diversity score at a time or optimize an approximation of the objective function.
• These approaches tend to be inefficient and are easily trapped in a suboptimal state.
• To address these challenges, we introduce Multi-Agent reinforcement learning (MARL) for search result DIVersity, which called MA4DIV.