Investigating User Behavior in Legal Case Retrieval

Legal case retrieval is a specialized IR task aiming to retrieve supporting cases given a query case. While recent research efforts are committed to improving the automatic retrieval models' performances, little attention has been paid to the practical search interactions between users and systems in this task. Therefore, we focus on investigating user behavior in the scenario of legal case retrieval. Specifically, we conducted a laboratory user study that involved 45 participants majoring in law to collect users' rich interactions and relevance assessments. With the collected data, we first analyzed the characteristics of the search process in legal case retrieval practice. We observed significant differences between legal case retrieval and general web search in various search behavior. These differences highlight the necessity of in-depth investigating user behavior in legal case retrieval and re-thinking the application of related mechanisms developed based on the user models in Web search. Then we investigated factors that would influence search behavior from different perspectives, including task difficulty and domain expertise. Finally, we shed light on implicit feedback in legal case retrieval and designed a predictive model for relevance based on user behavior. Our work provides a better understanding of user interactions in the legal case retrieval process, which can benefit the design of the corresponding retrieval systems to support legal practitioners.