DMCA
Modeling multi-step relevance propagation for expert finding (2008)
Cached
Download Links
- [wwwhome.cs.utwente.nl]
- [eprints.eemcs.utwente.nl]
- DBLP
Other Repositories/Bibliography
| Venue: | In CIKM ’08 |
| Citations: | 32 - 3 self |
BibTeX
@INPROCEEDINGS{Serdyukov08modelingmulti-step,
author = {Pavel Serdyukov and Henning Rode and Djoerd Hiemstra},
title = {Modeling multi-step relevance propagation for expert finding},
booktitle = {In CIKM ’08},
year = {2008}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
An expert finding system allows a user to type a simple text query and retrieve names and contact information of individuals that possess the expertise expressed in the query. This paper proposes a novel approach to expert finding in large enterprises or intranets by modeling candidate experts (persons), web documents and various relations among them with so-called expertise graphs. As distinct from the stateof-the-art approaches estimating personal expertise through one-step propagation of relevance probability from documents to the related candidates, our methods are based on the principle of multi-step relevance propagation in topicspecific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi-step relevance propagation improve over the baseline one-step propagation based method in almost all cases.
Keyphrases
multi-step relevance propagation expert finding probabilistic random walk related candidate relevance probability large organization topicspecific expertise graph so-called expertise graph candidate expert personal expertise simple text query various relation baseline one-step propagation novel approach contact information web document one-step propagation stateof-the-art approach trec enterprise track data large enterprise
