Generative Modeling of Persons and Documents for Expert Search

Pavel Serdyukov; Djoerd Hiemstra; M.M. Fokkinga; Peter M.G. Apers

doi:10.1145/1277741.1277929

Generative Modeling of Persons and Documents for Expert Search

Pavel Serdyukov, Djoerd Hiemstra, M.M. Fokkinga, Peter M.G. Apers

Databases (Former)

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

7 Citations (Scopus)

26 Downloads (Pure)

Abstract

In this paper we address the task of automatically finding an expert within the organization, known as the expert search problem. We present the theoretically-based probabilistic algorithm which models retrieved documents as mixtures of expert candidate language models. Experiments show that our approach outperforms existing theoretically sound solutions.

Original language	Undefined
Title of host publication	Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007)
Place of Publication	New York, NY, USA
Publisher	ACM Press
Pages	827-828
Number of pages	2
ISBN (Print)	978-1-59593-597-7
DOIs	https://doi.org/10.1145/1277741.1277929
Publication status	Published - Jul 2007
Event	30th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007 - Amsterdam, Netherlands Duration: 23 Jul 2007 → 27 Jul 2007 Conference number: 30

Publication series

Name
Number	128

Conference

Conference	30th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007
Abbreviated title	SIGIR
Country/Territory	Netherlands
City	Amsterdam
Period	23/07/07 → 27/07/07

Keywords

EWI-10422
METIS-241757
CR-H.3.3

Access to Document

10.1145/1277741.1277929

p827-serdyukov

http://doi.acm.org/10.1145/1277741.1277929

Cite this

@inproceedings{9704f989658b406083b656882ff2b1c3,

title = "Generative Modeling of Persons and Documents for Expert Search",

abstract = "In this paper we address the task of automatically finding an expert within the organization, known as the expert search problem. We present the theoretically-based probabilistic algorithm which models retrieved documents as mixtures of expert candidate language models. Experiments show that our approach outperforms existing theoretically sound solutions.",

keywords = "EWI-10422, METIS-241757, CR-H.3.3",

author = "Pavel Serdyukov and Djoerd Hiemstra and M.M. Fokkinga and Apers, {Peter M.G.}",

note = "10.1145/1277741.1277929 ; null ; Conference date: 23-07-2007 Through 27-07-2007",

year = "2007",

month = jul,

doi = "10.1145/1277741.1277929",

language = "Undefined",

isbn = "978-1-59593-597-7",

publisher = "ACM Press",

number = "128",

pages = "827--828",

booktitle = "Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007)",

}

Serdyukov, P, Hiemstra, D, Fokkinga, MM & Apers, PMG 2007, Generative Modeling of Persons and Documents for Expert Search. in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007). ACM Press, New York, NY, USA, pp. 827-828, 30th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, Amsterdam, Netherlands, 23/07/07. https://doi.org/10.1145/1277741.1277929

Generative Modeling of Persons and Documents for Expert Search. / Serdyukov, Pavel; Hiemstra, Djoerd; Fokkinga, M.M.; Apers, Peter M.G.

Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007). New York, NY, USA : ACM Press, 2007. p. 827-828.

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

TY - GEN

T1 - Generative Modeling of Persons and Documents for Expert Search

AU - Serdyukov, Pavel

AU - Hiemstra, Djoerd

AU - Fokkinga, M.M.

AU - Apers, Peter M.G.

N1 - Conference code: 30

PY - 2007/7

Y1 - 2007/7

N2 - In this paper we address the task of automatically finding an expert within the organization, known as the expert search problem. We present the theoretically-based probabilistic algorithm which models retrieved documents as mixtures of expert candidate language models. Experiments show that our approach outperforms existing theoretically sound solutions.

AB - In this paper we address the task of automatically finding an expert within the organization, known as the expert search problem. We present the theoretically-based probabilistic algorithm which models retrieved documents as mixtures of expert candidate language models. Experiments show that our approach outperforms existing theoretically sound solutions.

KW - EWI-10422

KW - METIS-241757

KW - CR-H.3.3

U2 - 10.1145/1277741.1277929

DO - 10.1145/1277741.1277929

M3 - Conference contribution

SN - 978-1-59593-597-7

SP - 827

EP - 828

BT - Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007)

PB - ACM Press

CY - New York, NY, USA

Y2 - 23 July 2007 through 27 July 2007

ER -