research-article

Fusion helps diversification

Authors:

Shangsong Liang,

Maarten de RijkeAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 303 - 312

https://doi.org/10.1145/2600428.2609561

Published: 03 July 2014 Publication History

Abstract

A popular strategy for search result diversification is to first retrieve a set of documents utilizing a standard retrieval method and then rerank the results. We adopt a different perspective on the problem, based on data fusion. Starting from the hypothesis that data fusion can improve performance in terms of diversity metrics, we examine the impact of standard data fusion methods on result diversification. We take the output of a set of rankers, optimized for diversity or not, and find that data fusion can significantly improve state-of-the art diversification methods. We also introduce a new data fusion method, called diversified data fusion, which infers latent topics of a query using topic modeling, without leveraging outside information. Our experiments show that data fusion methods can enhance the performance of diversification and DDF significantly outperforms existing data fusion methods in terms of diversity metrics.

References

[1]

S. Abbar, S. Amer-Yahia, P. Indyk, and S. Mahabadi. Real-time recommendation of diverse related articles. In WWW, pages 1--12, 2013.

Digital Library

[2]

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In WSDM, pages 5--14, 2009.

Digital Library

[3]

E. Aktolga and J. Allan. Sentiment diversification with different biases. In SIGIR, pages 593--600, 2013.

Digital Library

[4]

J. A. Aslam and M. Montague. Models for metasearch. In SIGIR'01, pages 276--284, 2001.

Digital Library

[5]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

[6]

J. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In SIGIR, pages 335--336, 1998.

Digital Library

[7]

H. Chen and D. R. Karger. Less is more: probabilistic models for retrieving fewer relevant documents. In SIGIR, pages 429--436, 2006.

Digital Library

[8]

C. L. A. Clarke and N. Craswell. Overview of the TREC 2011 web track. In TREC, pages 1--9, 2011.

[9]

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

Digital Library

[10]

C. L. A. Clarke, N. Craswell, and I. Soboroff. Overview of the TREC 2009 web track. In TREC, pages 1--9, 2009.

[11]

C. L. A. Clarke, N. Craswell, I. Soboroff, and G. V. Cormack. Overview of the TREC 2010 web track. In TREC, pages 1--9, 2010.

[12]

C. L. A. Clarke, N. Craswell, and E. M. Voorhees. Overview of the TREC 2012 web track. In TREC, pages 1--8, 2012.

[13]

V. Dang and W. B. Croft. Diversity by proportionality: An election-based approach to search result diversification. In SIGIR, pages 65--74, 2012.

Digital Library

[14]

V. Dang and W. B. Croft. Term level search result diversification. In SIGIR, pages 603--612, 2013.

Digital Library

[15]

M. Efron. Information search and retrieval in microblogs. J. Am. Soc. for Inform. Sci. and Techn., 62(6):996--1008, 2011.

Digital Library

[16]

M. Farah and D. Vanderpooten. An outranking approach for rank aggregation in information retrieval. In SIGIR'07, 2007.

Digital Library

[17]

E. A. Fox and J. A. Shaw. Combination of multiple searches. In TREC-2, 1994.

[18]

T. L. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101:5228--5235, 2004.

[19]

D. He and D. Wu. Toward a robust data fusion for document retrieval. In IEEE NLP-KE'08, 2008.

[20]

J. He, V. Hollink, and A. de Vries. Combining implicit and explicit topic representations for result diversification. In SIGIR, pages 851--860, 2012.

Digital Library

[21]

T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50--57, 1999.

Digital Library

[22]

O. Jin, N. N. Liu, K. Zhao, Y. Yu, and Q. Yang. Transferring topical knowledge from auxiliary long texts for short text clustering. In CIKM, pages 775--784, 2011.

Digital Library

[23]

A. K. Kozorovitsky and O. Kurland. Cluster-based fusion of retrieved lists. In SIGIR'11, pages 893--902, 2011.

Digital Library

[24]

T. Kurashima, T. Iwata, T. Hoshide, N. Takaya, and K. Fujimura. Geo topic model: joint modeling of user's activity area and interests for location recommendation. In WSDM, pages 375--384, 2013.

Digital Library

[25]

J. D. Lafferty and D. M. Blei. Correlated topic models. In NIPS'05, pages 147--154, 2005.

[26]

J. H. Lee. Combining multiple evidence from different properties of weighting schemes. In SIGIR'95, pages 180--188, 1995.

Digital Library

[27]

J. H. Lee. Analyses of multiple evidence combination. In SIGIR, 1997.

Digital Library

[28]

F. Li, M. Huang, and X. Zhu. Sentiment analysis with global topics and local dependency. In AAAI, 2010.

[29]

W. Li and A. McCallum. Pachinko allocation: Dag-structured mixture models of topic correlations. In ICML, pages 577--584. ACM, 2006.

Digital Library

[30]

S. Liang and M. de Rijke. Finding knowledgeable groups in enterprise corpora. In SIGIR'13, pages 1005--1008, 2013.

Digital Library

[31]

S. Liang, M. de Rijke, and M. Tsagkias. Late data fusion for microblog search. In ECIR'13, pages 743--746, 2013.

Digital Library

[32]

S. Liang, Z. Ren, and M. de Rijke. The impact of semantic document expansion on cluster-based fusion for microblog search. In ECIR'14, pages 493--499, 2014.

Digital Library

[33]

N. Limsopatham, R. McCreadie, and M.-D. Albakour. University of Glasgow at TREC 2012: Experiments with Terrier in medical records, microblog, and web tracks. In TREC, 2012.

[34]

J. S. Liu. The collapsed gibbs sampler in bayesian computations with applications to a gene regulation problem. J. Am. Stat. Assoc., 89(427):958--966, 1994.

[35]

C. Macdonald and I. Ounis. Voting for candidates: Adapting data fusion techniques for an expert search task. In CIKM, 2006.

Digital Library

[36]

Z. Ren, S. Liang, E. Meij, and M. de Rijke. Personalized time-aware tweets summarization. In SIGIR, 2013.

Digital Library

[37]

M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In UAI, pages 487--494, 2004.

Digital Library

[38]

T. Sakai, Z. Dou, and C. L. A. Clarke. The impact of intent selection on diversified search result. In SIGIR, 2013.

Digital Library

[39]

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

Digital Library

[40]

R. L. Santos, C. Macdonald, and I. Ounis. Intent-aware search result diversification. In SIGIR, pages 595--604, 2011.

Digital Library

[41]

R. L. T. Santos, J. Peng, C. Macdonald, and I. Ounis. Explicit search result diversification through sub-queries. In ECIR, 2010.

Digital Library

[42]

J. A. Shaw and E. A. Fox. Combination of multiple searches. In TREC 1992, pages 243--252. NIST, 1993.

[43]

D. Sheldon, M. Shokouhi, M. Szummer, and N. Craswell. LambdaMerge: merging the results of query reformulations. In WSDM, pages 795--804, 2011.

Digital Library

[44]

I. Szpektor, Y. Maarek, and D. Pelleg. When relevance is not enough: promoting diversity and freshness in personalized question recommendation. In WWW '13, 2013.

Digital Library

[45]

S. Vargas, P. Castells, and D. Vallet. Explicit relevance models in intent-oriented information retrieval diversification. In SIGIR, pages 75--84, 2012.

Digital Library

[46]

X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends. In KDD'06, pages 424--433, 2006.

Digital Library

[47]

X. Wei and W. B. Croft. LDA-based document models for ad-hoc retrieval. In SIGIR, pages 178--185, 2006.

Digital Library

[48]

X. Wei, J. Sun, and X. Wang. Dynamic mixture models for multiple time-series. In IJCAI, pages 2909--2914, 2007.

Digital Library

[49]

S. Wu. Data fusion in information retrieval, volume 13 of Adaptation, Learning and Optimization. Springer, 2012.

Digital Library

[50]

Z. Xu, Y. Zhang, Y. Wu, and Q. Yang. Modeling user posting behavior on social media. In SIGIR, pages 545--554, 2012.

Digital Library

[51]

C. Zhai, W. W. Cohen, and J. Lafferty. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In SIGIR, pages 10--17, 2003.

Digital Library

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Index Terms

Fusion helps diversification
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 03 July 2014

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SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhang RAi QWu YMa YLiu Y(2023)Result Diversification for Legal case RetrievalProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625319(158-168)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625319
Li CWang XZong TCao HLiu Y(2023)Predictive edge caching through deep mining of sequential patterns in user content retrievalsComputer Networks10.1016/j.comnet.2023.109866233(109866)Online publication date: Sep-2023
https://doi.org/10.1016/j.comnet.2023.109866
Cui YLi QKuo TXue C(2022)Online Rare Category Identification and Data Diversification for Edge ComputingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.308368141:5(1290-1301)Online publication date: May-2022
https://doi.org/10.1109/TCAD.2021.3083681
Bochsler D(2020)Measures of vote-seat disproportionality for incomplete dataParty Politics10.1177/135406882096838628:1(174-183)Online publication date: 29-Oct-2020
https://doi.org/10.1177/1354068820968386
Bernstein KRaiber FKurland OCulpepper JBalog KSetty VLioma CLiu YZhang MBerberich K(2020)Cluster-Based Document Retrieval with Multiple QueriesProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409825(33-40)Online publication date: 14-Sep-2020
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Benham RMackenzie JMoffat ACulpepper J(2019)Boosting Search Performance Using Query VariationsACM Transactions on Information Systems10.1145/334500137:4(1-25)Online publication date: 4-Oct-2019
https://dl.acm.org/doi/10.1145/3345001
Liang S(2019)Unsupervised Semantic Generative Adversarial Networks for Expert RetrievalThe World Wide Web Conference10.1145/3308558.3313625(1039-1050)Online publication date: 13-May-2019
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Harel SAlbo SAgichtein ERadinsky K(2019)Learning Novelty-Aware Ranking of Answers to Complex QuestionsThe World Wide Web Conference10.1145/3308558.3313457(2799-2805)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313457
Kurland OCulpepper JCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Fusion in Information RetrievalThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210186(1383-1386)Online publication date: 27-Jun-2018
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Liang SMarkov IRen Zde Rijke MChampin PGandon FMédini LLalmas MIpeirotis P(2018)Manifold Learning for Rank AggregationProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186085(1735-1744)Online publication date: 10-Apr-2018
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