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How Random Decisions Affect Selective Distributed Search

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SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 771 - 774

https://doi.org/10.1145/2766462.2767796

Published: 09 August 2015 Publication History

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Abstract

Selective distributed search is a retrieval architecture that reduces search costs by partitioning a corpus into topical shards such that only a few shards need to be searched for each query. Prior research created topical shards by using random seed documents to cluster a random sample of the full corpus. The resource selection algorithm might use a different random sample of the corpus. These random components make selective search non-deterministic. This paper studies how these random components affect experimental results. Experiments on two ClueWeb09 corpora and four query sets show that in spite of random components, selective search is stable for most queries.

References

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Cited By

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Hendriksen GHiemstra Dde Vries A(2024)Weighted AUReC: Handling Skew in Shard Map Quality Estimation for Selective SearchAdvances in Information Retrieval10.1007/978-3-031-56066-8_10(87-96)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_10
Kim YCallan JSong DLiu TSun LBruza PMelucci MSebastiani FYang G(2018)Measuring the Effectiveness of Selective Search Index Partitions without SupervisionProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234952(91-98)Online publication date: 10-Sep-2018
https://dl.acm.org/doi/10.1145/3234944.3234952
Dai ZXiong CCallan JMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Query-Biased Partitioning for Selective SearchProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983706(1119-1128)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983706

Index Terms

How Random Decisions Affect Selective Distributed Search
1. Information systems
  1. Information retrieval

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Published In

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2015

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National Science Foundation

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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

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Cited By

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Hendriksen GHiemstra Dde Vries A(2024)Weighted AUReC: Handling Skew in Shard Map Quality Estimation for Selective SearchAdvances in Information Retrieval10.1007/978-3-031-56066-8_10(87-96)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_10
Kim YCallan JSong DLiu TSun LBruza PMelucci MSebastiani FYang G(2018)Measuring the Effectiveness of Selective Search Index Partitions without SupervisionProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234952(91-98)Online publication date: 10-Sep-2018
https://dl.acm.org/doi/10.1145/3234944.3234952
Dai ZXiong CCallan JMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Query-Biased Partitioning for Selective SearchProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983706(1119-1128)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983706

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Query-Biased Partitioning for Selective Search

Load-Balancing in Distributed Selective Search

Selective Search: Efficient and Effective Search of Large Textual Collections