Understanding and Modeling Heterogeneous Search Behavior

We investigate between-user drivers of query variability through a controlled between-subject, full-factorial user study that manipulates age, gender, and language proficiency across six backstory-driven search tasks. From initial queries, session logs, and post-task interviews, we quantify how demographic and task factors shape query-, task-, and session-level behaviors. We further derive a small set of interpretable latent search dimensions from user evidence to analyze and simulate heterogeneous query behavior. Our results show that age is the most consistent predictor of query formulation and search interaction patterns. Gender and language differences are more selective, and task context is further associated with these patterns. The latent dimensions help explain the differences as variation in search strategy rather than uniform differences in engagement or ability. The paper provides a trait-informed view of heterogeneous search behavior that supports more user-aware analysis and robustness-oriented evaluation in IR.