Differences in the Use of Search Assistance for Tasks of Varying Complexity

Capra, Robert; Arguello, Jaime; Crescenzi, Anita; Vardell, Emily

doi:10.1145/2766462.2767741

Cited by 35 publications

(28 citation statements)

References 22 publications

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“…Participants are also more engaged in searching when interactive faceted query suggestions are available, and issue over twice as many interactions with the system when compared to the control condition. These results are in line with recent work by Luo, Zhang, and Yang (), Raman et al (), Capra et al (), and Vakkari and Huuskonen (), suggesting that search effort may increase in exploratory search, but searchers obtain increased satisfaction and session‐level effectiveness gains.…”

Section: Discussionsupporting

confidence: 91%

“…In general, it has been shown that interaction with search assistance is typically more frequent in cases of more complex tasks (Capra, Arguello, Crescenzi, & Vardell, ) and that users prefer specific hints over general ones. Users have a natural ability to recognize specific hints and query terms (Kangassalo, Spap, Jacucci, & Ruotsalo, ), and specific search hints have been demonstrated to effectively improve searcher success rates and reduce perceived effort, while generic search hints can be detrimental in both search effectiveness and user satisfaction (Savenkov & Agichtein, ).…”

Section: Related Workmentioning

confidence: 99%

“…Some studies using search engine logs have shown that search assistance may also raise concerns about the correctness and utility of the results. It has been observed that struggling and exploring behaviors are interleaved in web search (Hassan, White, Dumais, & Wang, ) and that increased usage of search assistance can be related to uncertainty of search success (Capra et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…Jiang et al, ; Li et al, ), query recommendation (Baeza‐yates, Hurtado, & Mendoza, ; Boldi et al, ), and intent prediction for reranking (Hu, Zhang, Chen, Wang, & Yang, ). Research has also explored task‐aware (Capra et al, ; Feild & Allan, ) and semantic models (Bing, Lam, Wong, & Jameel, ) for query recommendation.…”

Section: Related Workmentioning

confidence: 99%

“…Search assistance has been evaluated in restricted tasks, such as studying faceted classifications for filtering in known‐item search (Yee, Swearingen, Li, & Hearst, ) or clicks on other users' search trails (Capra, Arguello, Crescenzi, & Vardell, ). Despite the recognized importance of search assistance and the association of usage of assistance with session and task‐level factors (Capra, Arguello, Crescenzi, & Vardell, ), previous work has provided limited evidence on the effect of search assistance on users' retrieval performance. Consequently, our knowledge on how the availability of search assistance affects users' retrieval performance, momentarily at single query–response level and over time in longer exploratory search sessions, is fairly limited.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

Ruotsalo

Kaski

2019

Asso for Info Science & Tech

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The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole-session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed-query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole-session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole-session performance.

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Section: Discussionsupporting

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

Ruotsalo

Kaski

2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

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SMART: Search Maps and Routes/Trails of Both Subtasks and Sources to Support Complex Task‐based Search

Wang,

Zhang

et al. 2024

Proceedings of the Association for Information Science and Tech

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Towards the goal of constructing an agent that can assist users in completing complex tasks and searching for information from multiple sources. We propose a complex task‐supporting application, SMART (Search Maps and Routes/Trails), which combines complex task modeling through users' web activities and expands search support to include maps and routes/trails of both subtasks and sources within complex tasks through case‐based reasoning. We also summarize the results of a user study conducted to evaluate the usability of the SMART prototype system. The experimental results show that SMART could help searchers make search strategies, select information sources effectively, increase confidence during task performance, and improve search results. This highlights the value of both types of maps and routes/trails of subtasks and sources in complex task‐supporting.

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Predicting Search Task Difficulty through a Discrete‐Time Action Log Representation on Spectrum Kernel

Gacitúa

González‐Ibáñez

2022

Proceedings of the Association for Information Science and Tech

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Predicting perceived difficulty on a web search task is an open problem in the interactive information retrieval field. A common approach to tackle it, is through features obtained from full search sessions, which are then used to train classification models. In this poster we attempt to predict perceived task difficulty at different stages of the search process. To do so, we use the spectrum kernel for support vector machine (SVM) classification. Our preliminary results suggest that by using behavioral data from the first query segment, it is possible to provide timely classifications of whether a search task is perceived as hard or easy.

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Differences in the Use of Search Assistance for Tasks of Varying Complexity

Cited by 35 publications

References 22 publications

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

SMART: Search Maps and Routes/Trails of Both Subtasks and Sources to Support Complex Task‐based Search

Predicting Search Task Difficulty through a Discrete‐Time Action Log Representation on Spectrum Kernel

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