A probabilistic fusion framework

Yael Anava; Anna Shtok; Oren Kurland; Ella Rabinovich

doi:10.1145/2983323.2983739

A probabilistic fusion framework

Yael Anava, Anna Shtok, Oren Kurland, Ella Rabinovich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.

Original language	English
Title of host publication	CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	1463-1472
Number of pages	10
ISBN (Electronic)	9781450340731
DOIs	https://doi.org/10.1145/2983323.2983739
State	Published - 24 Oct 2016
Externally published	Yes
Event	25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
Volume	24-28-October-2016

Conference

Conference	25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/Territory	United States
City	Indianapolis
Period	24/10/16 → 28/10/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

ASJC Scopus subject areas

General Business, Management and Accounting
General Decision Sciences

Access to Document

10.1145/2983323.2983739

Cite this

Anava, Y., Shtok, A., Kurland, O., & Rabinovich, E. (2016). A probabilistic fusion framework. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (pp. 1463-1472). (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983739

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title = "A probabilistic fusion framework",

abstract = "There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.",

author = "Yael Anava and Anna Shtok and Oren Kurland and Ella Rabinovich",

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doi = "10.1145/2983323.2983739",

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Anava, Y, Shtok, A, Kurland, O & Rabinovich, E 2016, A probabilistic fusion framework. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, vol. 24-28-October-2016, Association for Computing Machinery, pp. 1463-1472, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 24/10/16. https://doi.org/10.1145/2983323.2983739

A probabilistic fusion framework. / Anava, Yael; Shtok, Anna; Kurland, Oren et al.
CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, 2016. p. 1463-1472 (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Anava, Yael

AU - Shtok, Anna

AU - Kurland, Oren

AU - Rabinovich, Ella

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AB - There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.

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A probabilistic fusion framework

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this