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Title:Investigating users’ learning and decision-making processes in search interactions: a behavioral economics approach
Author(s):Liu, Jiqun; Wang, Yiwei
Subject(s):Interactive information retrieval
Behavioral economics
Searching as learning
Abstract:How users think, learn, and make decisions when interacting with search systems is central to the area of Interactive Information Retrieval (IIR). Most of the prior work are either descriptive in nature or limited to one or two factors. The existing economic models of search illustrate a promising direction for developing formal models of users’ learning and search interactions. However, they were built upon numerous unrealistic assumptions about human capacity and rationality and ignored the impacts of cognitive biases (Liu & Shah, 2019). Thus, a fundamental question still persists: why do users learn and behave in the way they do in real-life situation? In this work, we seek to build and empirically test a behavioral economics framework, aiming to answer the question and address the limitations of previous studies by (1) linking the "isolated" insights from IIR studies together under a broader theoretical umbrella and also by (2) bridging IIR with the insights from behavioral economics and cognitive psychology. The behavioral economics model represents a "collision" between IIR and behavioral economics approach. This collision has multiple contributions: (1) it generates a concise representation of users' learning process and search interactions; (2) It offers space in the formal model for explaining the biases in users’ actual behavior; (3) It points to novel research questions regarding user modeling, recommendations design, and systems evaluation for future studies. Liu, J., & Shah, C. (2019). Investigating the impacts of expectation disconfirmation on Web search. In Proceedings of CHIIR (pp. 319-323). ACM. New York, NY.
Issue Date:2019-09-24
Series/Report:Interactive information retrieval
Information seeking
Information needs
Genre:Conference Poster
Type:Text
URI:http://hdl.handle.net/2142/105387
Date Available in IDEALS:2019-08-23


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