Pseudo-relevance feedback research develops methods for automatic query expansion and retrieval improvement without explicit user feedback. Key contributions include condensed list relevance models that provide nearly identical performance to re-retrieval with significantly lower latency, and pseudo-query reformulation approaches that treat query modification as a graph search problem. These methods demonstrate consistent improvements over baseline retrieval algorithms while maintaining efficiency and addressing the stability issues common in traditional pseudo-relevance feedback approaches.