Article

Depth- and breadth-first processing of search result lists

Authors:

Kerstin Klöckner,

Nadine Wirschum,

Anthony JamesonAuthors Info & Claims

CHI EA '04: CHI '04 Extended Abstracts on Human Factors in Computing Systems

Page 1539

https://doi.org/10.1145/985921.986115

Published: 24 April 2004 Publication History

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Abstract

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References

[1]

S. Dumais, E. Cutrell, and H. Chen. Optimizing search by showing results in context. In Proc. CHI 2002, pages 277--284. ACM Press, 2001.

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[2]

A. Tombros and M. Sanderson. Advantages of query biased summaries in information retrieval. In Proc. SIGIR, pages 2--10. ACM Press, 1998.

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Cited By

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Chen JDong HWang XFeng FWang MHe X(2023)Bias and Debias in Recommender System: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/356428441:3(1-39)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3564284
Färber MCoutinho MYuan S(2023)Biases in scholarly recommender systems: impact, prevalence, and mitigationScientometrics10.1007/s11192-023-04636-2128:5(2703-2736)Online publication date: 21-Mar-2023
https://doi.org/10.1007/s11192-023-04636-2
Gwadabe TAl-hababi MLiu Y(2023)SimGNN: simplified graph neural networks for session-based recommendationApplied Intelligence10.1007/s10489-023-04719-w53:19(22789-22802)Online publication date: 3-Jul-2023
https://doi.org/10.1007/s10489-023-04719-w
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Index Terms

Depth- and breadth-first processing of search result lists
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Multi / mixed media creation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Hypertext / hypermedia

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Published In

CHI EA '04: CHI '04 Extended Abstracts on Human Factors in Computing Systems

April 2004

975 pages

ISBN:1581137036

DOI:10.1145/985921

Conference Chairs:
Elizabeth Dykstra-Erickson
Kinoma
,
Manfred Tscheligi
CURE, Austria

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Association for Computing Machinery

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Published: 24 April 2004

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CHI04: CHI 2004 Conference on Human Factors in Computing Systems

April 24 - 29, 2004

Vienna, Austria

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Chen JDong HWang XFeng FWang MHe X(2023)Bias and Debias in Recommender System: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/356428441:3(1-39)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3564284
Färber MCoutinho MYuan S(2023)Biases in scholarly recommender systems: impact, prevalence, and mitigationScientometrics10.1007/s11192-023-04636-2128:5(2703-2736)Online publication date: 21-Mar-2023
https://doi.org/10.1007/s11192-023-04636-2
Gwadabe TAl-hababi MLiu Y(2023)SimGNN: simplified graph neural networks for session-based recommendationApplied Intelligence10.1007/s10489-023-04719-w53:19(22789-22802)Online publication date: 3-Jul-2023
https://doi.org/10.1007/s10489-023-04719-w
Abualsaud MSmucker MElsweiler DKruschwitz ULudwig B(2022)The Dark Side of Relevance: The Effect of Non-Relevant Results on Search BehaviorProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505770(1-11)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505770
Gwadabe TLiu Y(2022)IC-GAR: item co-occurrence graph augmented session-based recommendationNeural Computing and Applications10.1007/s00521-021-06859-x34:10(7581-7596)Online publication date: 8-Jan-2022
https://doi.org/10.1007/s00521-021-06859-x
Gwadabe TLiu Y(2021)Improving graph neural network for session-based recommendation system via non-sequential interactionsNeurocomputing10.1016/j.neucom.2021.10.034468:C(111-122)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.neucom.2021.10.034
Eide SPoljac ASandnes F(2021)Image Search Versus Text Search Revisited: A Simple Experiment Using a Kahoot QuizUniversal Access in Human-Computer Interaction. Design Methods and User Experience10.1007/978-3-030-78092-0_12(186-196)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-78092-0_12
Strzelecki A(2020)Eye-Tracking Studies of Web Search Engines: A Systematic Literature ReviewInformation10.3390/info1106030011:6(300)Online publication date: 3-Jun-2020
https://doi.org/10.3390/info11060300
Santini S(2020)Assigning Rated Items to Locations in Non-List Display LayoutsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.287016426:2(1278-1291)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TVCG.2018.2870164
Niu XFan XFang YZhang YAllan JBalog KCarterette BGuo J(2019)Deep Learning of Human Information Foraging Behavior with a Search EngineProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3344231(185-192)Online publication date: 26-Sep-2019
https://dl.acm.org/doi/10.1145/3341981.3344231
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Best-First vs. Depth-First AND/OR Search for Multi-objective Constraint Optimization

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Breadth-first heuristic search

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Best-First vs. Depth-First AND/OR Search for Multi-objective Constraint Optimization

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