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theses

Previous work in interactive information retrieval (IIR) has explored the relationships between individuals’ search behavior, the characteristics of their search tasks, and their perceptions of their tasks, such as perceived topic familiarity and task difficulty. This work ultimately serves goals like personalization and search satisfaction. It is believed that predictions of task characteristics or searcher characteristics from observed behavior can help tailor search experiences to support task completion and search satisfaction. Often, research examines changes in behaviors when one or two characteristics change at a time. It applies methods such as t-tests, ANOVAs, and multivariate regression. This dissertation shows the limitations of this empirical framework. The contribution of this dissertation is in demonstrating that task characteristics, user characteristics, and behaviors should be empirically studied as a network of dependencies. It expands empirical work using graphical modeling, which can uniquely capture phenomena such as mediation and conditional independence. Research questions regarding mediation and conditional independence can hence now be answered with this different framework. This dissertation empirically shows when knowledge about behavior and certain task characteristics can be used to learn about other aspects of the task. It shows how task and user characteristics simultaneously affect behavior while potentially affecting each other. Specifically applying path analysis and Bayesian structure learning, results are shown to agree well with past literature and to also extend our understanding of the information seeking process. This dissertation discusses and shows the benefits and challenges of this modeling approach.

ETD_9322

doi:10.7282/t3-pg46-y768

Ph.D.

Includes bibliographical references

ETD doctoral

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