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  Unsupervised Prediction of Citation Influences

Dietz, L., Bickel, S., & Scheffer, T. (2007). Unsupervised Prediction of Citation Influences. In Z. Ghahramani (Ed.), ICML'07 (pp. 233-240). New York, NY: ACM. doi:10.1145/1273496.1273526.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-2126-1 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-C110-0
Genre: Conference Paper

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 Creators:
Dietz, Laura1, Author           
Bickel, Steffen2, Author           
Scheffer, Tobias2, Author           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2Machine Learning, MPI for Informatics, Max Planck Society, ou_1116552              

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 Abstract: Abstract Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.

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Language(s): eng - English
 Dates: 2008-03-2620072007
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 356448
DOI: 10.1145/1273496.1273526
Other: Local-ID: C12573CC004A8E26-7BB3909FC51FCC05C12572D0002F9668-Dietz2006
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Title: 24th International Conference on Machine Learning
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML'07
  Abbreviation : ICML 2007
  Subtitle : Proceedings of the Twenty-Fourth International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Zoubin1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 233 - 240 Identifier: ISBN: 978-1-59593-793-3