An information retrieval benchmarking model of satisficing and impatient users’ behavior in online search environments

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This study analyzes the effects that the position of the alternatives ranked by a search engine and the relative impatience of users have on their information retrieval behavior. We design a stochastic information retrieval algorithm calibrated to mimic the click-through rates (CTRs) of users observed in real-life environments. We introduce two versions of the mimicking algorithm designed to demonstrate the importance of impatience as a determinant of CTRs conditioned by the alternatives’ ranking position. The first version assumes that users proceed sequentially through the ranking until they find an alternative satisficing their expectations. Once they find a satisficing alternative, they continue retrieving information until they observe an alternative that violates their expectations. The second version increases users’ impatience, who stop retrieving information as soon as an alternative does not satisfy their expectations – even if it is the top-ranked one. All three algorithmic structures are sufficiently malleable to incorporate any potential modification to users’ beliefs and preferences. We simulate sets of 1,000,000 queries to illustrate how the CTRs of the top three ranked alternatives remain stable as users grow impatient, with differences widening as growingly impatient users proceed halfway through the ranking.




Di Caprio, D., Santos-Arteaga, F.J. and Tavana, M. (2022) ‘An Information Retrieval Benchmarking Model of Satisficing and Impatient Users’ Behavior in Online Search Environments,’ Expert Systems with Applications, Vol. 191, Article 116352.