research-article

Extending Faceted Search to the General Web

Authors:

James AllanAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 839 - 848

https://doi.org/10.1145/2661829.2661964

Published: 03 November 2014 Publication History

Abstract

Faceted search helps users by offering drill-down options as a complement to the keyword input box, and it has been used successfully for many vertical applications, including e-commerce and digital libraries. However, this idea is not well explored for general web search, even though it holds great potential for assisting multi-faceted queries and exploratory search. In this paper, we explore this potential by extending faceted search into the open-domain web setting, which we call Faceted Web Search. To tackle the heterogeneous nature of the web, we propose to use query-dependent automatic facet generation, which generates facets for a query instead of the entire corpus. To incorporate user feedback on these query facets into document ranking, we investigate both Boolean filtering and soft ranking models. We evaluate Faceted Web Search systems by their utility in assisting users to clarify search intent and find subtopic information. We describe how to build reusable test collections for such tasks, and propose an evaluation method that considers both gain and cost for users. Our experiments testify to the potential of Faceted Web Search, and show Boolean filtering feedback models, which are widely used in conventional faceted search, are less effective than soft ranking models.

References

[1]

N. Abdul-Jaleel, J. Allan, W. B. Croft, F. Diaz, L. Larkey, X. Li, M. D. Smucker, and C. Wade. Umass at trec 2004: Novelty and hard. Technical report, DTIC Document, 2004.

[2]

E. Agirre, E. Alfonseca, K. Hall, J. Kravalova, M. Paşca, and A. Soroa. A study on similarity and relatedness using distributional and wordnet-based approaches. In Proc. of NAACL-HLT, pages 19--27, 2009.

Digital Library

[3]

J. Allan. Relevance feedback with too much data. In Proc. of SIGIR, pages 337--343, 1995.

Digital Library

[4]

C. Buckley, G. Salton, J. Allan, and A. Singhal. Automatic query expansion using smart: Trec 3. NIST special publication, pages 69--69, 1995.

[5]

R. D. Burke, K. J. Hammond, and B. C. Young. Knowledge-based navigation of complex information spaces. In Proc. of National Conference of Artificial Intelligence, 1996.

Digital Library

[6]

C. Carpineto, S. Osiński, G. Romano, and D. Weiss. A survey of web clustering engines. ACM Computing Surveys (CSUR), 41(3):17, 2009.

Digital Library

[7]

C. L. Clarke, N. Craswell, and I. Soboro. Overview of the trec 2009 web track. Technical report, DTIC Document, 2009.

[8]

C. L. Clarke, N. Craswell, I. Soboro, and G. V. Cormack. Overview of the trec 2010 web track. Technical report, DTIC Document, 2009.

[9]

C. L. Clarke, N. Craswell, I. Soboro, and E. M. Voorhees. Overview of the trec 2011 web track. Technical report, DTIC Document, 2009.

[10]

C. L. Clarke, N. Craswell, and E. M. Voorhees. Overview of the trec 2012 web track. Technical report, DTIC Document, 2009.

[11]

C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. In Proc. of SIGIR, pages 659--666, 2008.

Digital Library

[12]

G. V. Cormack, M. D. Smucker, and C. L. Clarke. Efficient and effective spam filtering and re-ranking for large web datasets. Information retrieval, 14(5):441--465, 2011.

Digital Library

[13]

W. Dakka and P. G. Ipeirotis. Automatic extraction of useful facet hierarchies from text databases. In Proc. of ICDE, pages 466--475, 2008.

Digital Library

[14]

V. Dang, X. Xue, and W. B. Croft. Inferring query aspects from reformulations using clustering. In Proc. of CIKM, pages 2117--2120, 2011.

Digital Library

[15]

D. Dash, J. Rao, N. Megiddo, A. Ailamaki, and G. Lohman. Dynamic faceted search for discovery-driven analysis. In Proc. of CIKM, pages 3--12, 2008.

Digital Library

[16]

Z. Dou, S. Hu, Y. Luo, R. Song, and J.-R. Wen. Finding dimensions for queries. In Proc. of CIKM, pages 1311--1320, 2011.

Digital Library

[17]

J. English, M. Hearst, R. Sinha, K. Swearingen, and K.-P. Yee. Hierarchical faceted metadata in site search interfaces. In Proc. of CHI, pages 628--639, 2002.

Digital Library

[18]

M. Hearst. Design recommendations for hierarchical faceted search interfaces. In SIGIR Workshop on Faceted Search.

[19]

M. Hearst. UIs for Faceted Navigation: Recent Advances and Remaining Open Problems. In Workshop on Computer Interaction and Information Retrieval, HCIR, 2008.

[20]

L. Heyer, S. Kruglyak, and S. Yooseph. Exploring expression data: identification and analysis of coexpressed genes. Genome research, 9(11):1106--1115, 1999.

[21]

A. Jain and G. Mishne. Organizing query completions for web search. In Proc. of CIKM, pages 1169--1178, 2010.

Digital Library

[22]

D. Klein and C. D. Manning. Accurate unlexicalized parsing. In Proc. of ACL, pages 423--430. Association for Computational Linguistics, 2003.

Digital Library

[23]

J. Koenemann and N. J. Belkin. A case for interaction: a study of interactive information retrieval behavior and effectiveness. In Proc. of SIGCHI, pages 205--212, 1996.

Digital Library

[24]

C. Kohlschütter, P.-A. Chirita, and W. Nejdl. Using link analysis to identify aspects in faceted web search. In SIGIR Workshop on Faceted Search, 2006.

[25]

W. Kong and J. Allan. Extracting query facets from search results. In Proc. of SIGIR, pages 93--102, 2013.

Digital Library

[26]

J. Koren, Y. Zhang, and X. Liu. Personalized interactive faceted search. In Proc. of WWW, pages 477--486, 2008.

Digital Library

[27]

R. Krovetz. Viewing morphology as an inference process. In Proc. of SIGIR, pages 191--202, 1993.

Digital Library

[28]

B. Kules, R. Capra, M. Banta, and T. Sierra. What do exploratory searchers look at in a faceted search interface? In Proc. of JCDL, pages 313--322, 2009.

Digital Library

[29]

K. Latha, K. R. Veni, and R. Rajaram. Afgf: An automatic facet generation framework for document retrieval. In Proc. of ACE, pages 110--114. IEEE, 2010.

Digital Library

[30]

D. Lawrie, W. B. Croft, and A. Rosenberg. Finding topic words for hierarchical summarization. In Proc. of SIGIR, pages 349--357, 2001.

Digital Library

[31]

D. J. Lawrie and W. B. Croft. Generating hierarchical summaries for web searches. In Proc. of SIGIR, pages 457--458, 2003.

Digital Library

[32]

C. Li, N. Yan, S. B. Roy, L. Lisham, and G. Das. Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia. In Proc. of WWW, pages 651--660, 2010.

Digital Library

[33]

D. Metzler and W. B. Croft. A markov random field model for term dependencies. In Proc. of SIGIR, pages 472--479, 2005.

Digital Library

[34]

C. G. Nevill-Manning, I. H. Witten, and G. W. Paynter. Lexically-generated subject hierarchies for browsing large collections. International Journal on Digital Libraries, 2(2-3):111--123, 1999.

Digital Library

[35]

E. Oren, R. Delbru, and S. Decker. Extending faceted navigation for rdf data. In Proc. of ISWC, pages 559--572, 2006.

Digital Library

[36]

P. Pantel, E. Crestan, A. Borkovsky, A.-M. Popescu, and V. Vyas. Web-scale distributional similarity and entity set expansion. In Proc. of EMNLP, pages 938--947, 2009.

Digital Library

[37]

P. Pantel and D. Lin. Discovering word senses from text. In Proc. of SIGKDD, pages 613--619, 2002.

Digital Library

[38]

P. Pantel, D. Ravichandran, and E. Hovy. Towards terascale knowledge acquisition. In Proc. of ICCL, page 771. Association for Computational Linguistics, 2004.

Digital Library

[39]

J. J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The Smart retrieval system - experiments in automatic document processing, pages 313--323. Englewood Cliffs, NJ: Prentice-Hall, 1971.

[40]

T. Sakai and R. Song. Evaluating diversified search results using per-intent graded relevance. In Proc. of SIGIR, pages 1043--1052, 2011.

Digital Library

[41]

G. Salton. Improving retrieval performance by relevance feedback. Readings in information retrieval, 24:5, 1997.

Digital Library

[42]

R. L. Santos, C. Macdonald, and I. Ounis. Exploiting query reformulations for web search result diversification. In Proc. of WWW, pages 881--890, 2010.

Digital Library

[43]

A. Schuth and M. Marx. Evaluation methods for rankings of facetvalues for faceted search. In Multilingual and Multimodal Information Access Evaluation, pages 131--136. 2011.

Digital Library

[44]

S. Shi, H. Zhang, X. Yuan, and J.-R. Wen. Corpus-based semantic class mining: distributional vs. pattern-based approaches. In Proc. of ICCL, pages 993--1001, 2010.

Digital Library

[45]

R. Song, M. Zhang, T. Sakai, M. Kato, Y. Liu, M. Sugimoto, Q. Wang, and N. Orii. Overview of the ntcir-9 intent task. In Proc. of NTCIR-9 Workshop Meeting, pages 82--105, 2011.

[46]

E. Stoica and M. A. Hearst. Automating creation of hierarchical faceted metadata structures. In In Procs. of NAACL-HLT, 2007.

[47]

B. Tan, A. Velivelli, H. Fang, and C. Zhai. Term feedback for information retrieval with language models. In Proc. of SIGIR, pages 263--270, 2007.

Digital Library

[48]

J. Teevan, S. Dumais, and Z. Gutt. Challenges for supporting faceted search in large, heterogeneous corpora like the web. Proc. of HCIR, pages 6--8, 2008.

[49]

X. Wang, D. Chakrabarti, and K. Punera. Mining broad latent query aspects from search sessions. In Proc. of SIGKDD, pages 867--876, 2009.

Digital Library

[50]

F. Wu, J. Madhavan, and A. Y. Halevy. Identifying aspects for web-search queries. JAIR, 40:677--700, 2011.

Digital Library

[51]

J. Xu and W. B. Croft. Query expansion using local and global document analysis. In Proc. of SIGIR, pages 4--11, 1996.

Digital Library

[52]

H. Zhang, M. Zhu, S. Shi, and J.-R. Wen. Employing topic models for pattern-based semantic class discovery. In Proc. of the ACL-IJCNLP, pages 459--467, 2009.

Digital Library

[53]

L. Zhang and Y. Zhang. Interactive retrieval based on faceted feedback. In Proc. of SIGIR, pages 363--370, 2010.

Digital Library

Cited By

Liu MWu TChen TLi FKittur AMyers B(2024)Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642149(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642149
Zhao ZDou ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Generating Multi-turn Clarification for Web Information SeekingProceedings of the ACM Web Conference 202410.1145/3589334.3645712(1539-1548)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645712
Liu WZhao ZZhu YDou ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Mining Exploratory Queries for Conversational SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645424(1386-1394)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645424
Show More Cited By

Index Terms

Extending Faceted Search to the General Web
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

Personalized interactive faceted search
WWW '08: Proceedings of the 17th international conference on World Wide Web

Faceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with key-value metadata that is used for query refinement. While popular in e-commerce and ...
Extending Faceted Search to the Open-Domain Web

Faceted search enables users to navigate a multi-dimensional information space by combining keyword search with drill-down options in each facets. For example, when searching "computer monitor" in an e-commerce site, users can select brands and monitor ...
A survey of faceted search

Faceted Search is an exploratory search mechanism, which provides an iterative way to refine search results by a faceted taxonomy. With the benefit of search results diversification, no need for a priori knowledge, and never leading to zero result, it ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Defense Advanced Research Projects Agency
International Business Machines Corporation
Center for Intelligent Information Retrieval

Conference

CIKM '14

Sponsor:

CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

Acceptance Rates

CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
313
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu MWu TChen TLi FKittur AMyers B(2024)Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642149(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642149
Zhao ZDou ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Generating Multi-turn Clarification for Web Information SeekingProceedings of the ACM Web Conference 202410.1145/3589334.3645712(1539-1548)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645712
Liu WZhao ZZhu YDou ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Mining Exploratory Queries for Conversational SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645424(1386-1394)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645424
Ali ECaputo AJones G(2023)A Comprehensive Survey of Facet Ranking Approaches Used in Faceted Search SystemsInformation10.3390/info1407038714:7(387)Online publication date: 7-Jul-2023
https://doi.org/10.3390/info14070387
Vakkari P(2023)Information Search Patterns in Complex TasksSRELS Journal of Information Management10.17821/srels/2023/v60i1/170892(19-30)Online publication date: 27-Mar-2023
https://doi.org/10.17821/srels/2023/v60i1/170892
Ni SBi KGuo JCheng X(2023)A Comparative Study of Training Objectives for Clarification Facet GenerationProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625332(1-10)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625332
Zhao ZDou ZGuo YCao ZCheng XSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Improving Search Clarification with Structured Information Extracted from Search ResultsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599389(3549-3558)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599389
Liu X(2023)Query Sub-intent Mining by Incorporating Search Results with Query Logs for Information Retrieval2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA57405.2023.10104948(180-186)Online publication date: 3-Mar-2023
https://doi.org/10.1109/ICBDA57405.2023.10104948
Hashemi HZamani HCroft WAl Hasan MXiong L(2022)Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent RepresentationsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557666(4003-4008)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557666
Yu PRahimi RAllan JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Towards Explainable Search ResultsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532067(669-680)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532067
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents