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Abstract Understanding the roles ofsearch gainandcostin users' search decision‐making is a key topic in interactive information retrieval (IIR). While previous research has developed user models based onsimulatedgains and costs, it is unclear how users' actualperceptions of search gains and costsform and change during search interactions. To address this gap, our study adopted expectation‐confirmation theory (ECT) to investigate users' perceptions of gains and costs. We re‐analyzed data from our previous study, examining how contextual and search features affect users' perceptions and how their expectation‐confirmation states impact their following searches. Our findings include: (1) The point where users' actual dwell time meets their constant expectation may serve as a reference point in evaluating perceived gain and cost; (2) these perceptions are associated with in situ experience represented by usefulness labels, browsing behaviors, and queries; (3) users' current confirmation states affect their perceptions of Web page usefulness in the subsequent query. Our findings demonstrate possible effects of expectation‐confirmation, prospect theory, and information foraging theory, highlighting the complex relationships among gain/cost, expectations, and dwell time at the query level, and the reference‐dependent expectation at the session level. These insights enrich user modeling and evaluation in human‐centered IR.
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Deep Learning of Human Information Foraging Behavior with a Search Engine
In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.
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- Award ID(s):
- 1910696
- PAR ID:
- 10188328
- Date Published:
- Journal Name:
- Proceedings of 2019 ACM SIGIR International Conference on the Theory of Information Retrieval
- Page Range / eLocation ID:
- 185 to 192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341981.3344231
