Our Ph.D. student, Jiqun Liu, has successfully defended his dissertation titled “A State-Based Approach to Supporting Users in Complex Search Tasks”. The committee included Chirag Shah (University of Washington, Chair), Nick Belkin (Rutgers University), Kaitlin Costello (Rutgers University), and Dan Russell (Google).
Liu’s study focuses on understanding the multi-round search processes of complex search tasks by using computational models of interactive IR and develop personalized recommendations to support task completion and search satisfaction. From the study, the team built a search recommendation model based on Q-learning algorithm. The results demonstrated that the simulated search episodes can improve search efficiency to many extents.
Previous work on task-based interactive information retrieval (IR) has mainly focused on what users found along the search process and the predefined, static aspects of complex search tasks (e.g., task goal, task product, cognitive task complexity), rather than how complex search tasks of different types can be better understood, examined, and disambiguated within the associated multi-round search processes. Also, it is believed that the knowledge about users’ cognitive variations in task-based search process can help tailor search paths and experiences to support task completion and search satisfaction. To adaptively support users engaging in complex search tasks, it is critical to connect theoretical, descriptive frameworks of search process with computational models of interactive IR and develop personalized recommendations for users according to their task states. Based on the data collected from two laboratory user studies, in this dissertation we sought to understand the states and state transition patterns in complex search tasks of different types and predict the identified task states using Machine Learning (ML) classifiers built upon observable search behavioral features. Moreover, through running Q-learning-based simulation of adaptive search recommendations, we also explored how the state-based framework could be applied in building computational models and supporting users with timely recommendations.
Based on the results from the dissertation study, we identified four intention-based task states and six problem-help-based task states, which depict the active, planned dimension and situational, unanticipated dimension of search tasks respectively. We also found that 1) task state transition patterns as features extracted from interaction process could be useful for disambiguating different types of search tasks; 2) the implicit task states can be inferred and predicted using behavioral-feature-based ML classifiers. With respect to application, we built a search recommendation model based on Q-learning algorithm and the knowledge we learned about task states. Then we apply the model in simulating search sessions consisting of potentially useful query segments with high rewards from different users. Our results demonstrated that the simulated search episodes can improve search efficiency to varying extents in different task scenarios. However, in many task contexts, this improvement often comes with the price of hurting the diversity and fairness in information coverage.
This dissertation presents a comprehensive study on state-based approach to understanding and supporting complex search tasks: from task state and state transition pattern identification, task state prediction, all the way to the application of computational state-based model in simulating dynamic search recommendations. Our process-oriented, state-based framework can be further extended with studies in a variety of contexts (e.g., multi-session search, collaborative search, conversational search) and deeper knowledge about users’ cognitive limits and search decision-making.