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Article

Voting for candidates: adapting data fusion techniques for an expert search task

Authors:

Craig Macdonald,

Iadh OunisAuthors Info & Claims

CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management

Pages 387 - 396

https://doi.org/10.1145/1183614.1183671

Published: 06 November 2006 Publication History

Abstract

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.

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Index Terms

Voting for candidates: adapting data fusion techniques for an expert search task
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Conferences

CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management

November 2006

916 pages

ISBN:1595934332

DOI:10.1145/1183614

General Chair:
Philip S. Yu
IBM T.J. Watson Research Center (USA)
,
Program Chairs:
Vassilis Tsotras
University of California-Riverside (USA)
,
Edward Fox
Virginia Tech (USA)
,
Bing Liu
University of Illinois at Chicago (USA)

Copyright © 2006 ACM.

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 ACM 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]

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Published: 06 November 2006

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November 6 - 11, 2006

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

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Pei WZhou PHuang JSun GLiu J(2024)State recognition and temperature rise time prediction of tobacco curing using multi-sensor data-fusion method based on feature impact factorExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121591237:PCOnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121591
Arabzadeh NGolzadeh KRisi CClarke CZhao J(2024)KnowFIRES: A Knowledge-Graph Framework for Interpreting Retrieved Entities from SearchAdvances in Information Retrieval10.1007/978-3-031-56069-9_15(182-188)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_15
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