The Effect of Task Complexity and Domain Expertise on Search Performance and User Behavior in Conversational Exploratory Search

As online resources expand, users increasingly engage in complex search tasks to acquire knowledge or explore unfamiliar topics. Supporting dynamic and interactive search behaviors remains challenging for current search engines, but conversational search offers a promising solution. In conversational search, users express intent naturally through spoken language and engage in multi-turn interactions to progressively satisfy their information needs. Previous studies on traditional web search have shown that task complexity and domain expertise significantly influence user behavior and performance. However, it remains unclear whether these factors exert different effects on user behavior and performance in conversational search, particularly in exploratory tasks. In this study, we conducted a carefully designed user experiment with 32 participants and collected a comprehensive dataset of search behaviors, including querying, clicking, and eye fixation sequences, along with explicit feedback and questionnaire data. Using this dataset, we investigated the influence of task complexity and domain expertise on search performance and user behavior in conversational exploratory search, focusing on three interactive aspects: user effort, user queries, and user clicks. Our findings provide valuable insights into how task complexity and expertise influence user behavior and search performance, with implications for optimizing conversational systems to better support complex exploratory tasks.