Temporal Dynamics

Temporal dynamics research examines how time affects information retrieval and ranking, focusing on the temporal characteristics of queries, documents, and user information needs. This work includes developing temporal profiles of queries to predict retrieval performance, creating recency-sensitive ranking algorithms that balance relevance with freshness, and leveraging social media data to improve real-time web search. Key contributions involve detecting recency-sensitive queries, incorporating temporal features into machine learning models for ranking, and understanding how document timestamps can characterize topic temporality. The research addresses both the technical challenges of ranking fresh content and the evaluation methodologies needed for time-sensitive information access systems.