research-article

Faceted Search with Object Ranking and Answer Size Constraints

Authors:

Kostas Manioudakis,

Yannis TzitzikasAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 1

Article No.: 9, Pages 1 - 33

https://doi.org/10.1145/3425603

Published: 16 November 2020 Publication History

Abstract

Faceted Search is a widely used interaction scheme in digital libraries, e-commerce, and recently also in Linked Data. Surprisingly, object ranking in the context of Faceted Search is not well studied in the literature. In this article, we propose an extension of the model with two parameters that enable specifying the desired answer size and the granularity of the sought object ranking. These parameters allow tackling the problem of too big or too small answers and can specify how refined the sought ranking should be. Then, we provide an algorithm that takes as input these parameters and by considering the hard-constraints (filters), the soft-constraints (preferences), as well as the statistical properties of the dataset (through various frequency-based ranking schemes), produces an object ranking that satisfies these parameters, in a transparent way for the user. Then, we present extensive simulation-based evaluation results that provide evidence that the proposed model also improves the answers and reduces the user’s cost. Finally, we propose GUI extensions that are required and present an implementation of the model.

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Cited By

Papadaki MTzitzikas Y(2024)Unifying Faceted Search and Analytics over RDF Knowledge GraphsKnowledge and Information Systems10.1007/s10115-024-02076-966:7(3921-3958)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02076-9
He CMicallef LSerim BVuong TRuotsalo TJacucci G(2023)Interactive visual facets to support fluid exploratory searchJournal of Visualization10.1007/s12650-022-00865-426:1(211-230)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s12650-022-00865-4
Mahdi MAhmad ANatiq HSubhi MQassim Q(2021)Comprehensive Review and Future Research Directions on Dynamic Faceted SearchApplied Sciences10.3390/app1117811311:17(8113)Online publication date: 31-Aug-2021
https://doi.org/10.3390/app11178113
Show More Cited By

Index Terms

Faceted Search with Object Ranking and Answer Size Constraints
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
2. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 1

January 2021

329 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3423044

Editor:
Min Zhang
Tsinghua University, China

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Copyright © 2020 ACM.

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Publication History

Published: 16 November 2020

Accepted: 01 September 2020

Revised: 01 August 2020

Received: 01 December 2019

Published in TOIS Volume 39, Issue 1

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Cited By

Papadaki MTzitzikas Y(2024)Unifying Faceted Search and Analytics over RDF Knowledge GraphsKnowledge and Information Systems10.1007/s10115-024-02076-966:7(3921-3958)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10115-024-02076-9
He CMicallef LSerim BVuong TRuotsalo TJacucci G(2023)Interactive visual facets to support fluid exploratory searchJournal of Visualization10.1007/s12650-022-00865-426:1(211-230)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s12650-022-00865-4
Mahdi MAhmad ANatiq HSubhi MQassim Q(2021)Comprehensive Review and Future Research Directions on Dynamic Faceted SearchApplied Sciences10.3390/app1117811311:17(8113)Online publication date: 31-Aug-2021
https://doi.org/10.3390/app11178113
Heidari GRamadan AStocker MAuer S(2021)Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital LibrariesLinking Theory and Practice of Digital Libraries10.1007/978-3-030-86324-1_18(141-152)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86324-1_18

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