Unsupervised Prediction of Citation Influences

Dietz, Laura; Bickel, Steffen; Scheffer, Tobias

doi:10.1145/1273496.1273526

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Unsupervised Prediction of Citation Influences

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Dietz, Laura
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Bickel, Steffen
Machine Learning, MPI for Informatics, Max Planck Society;

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Scheffer, Tobias
Machine Learning, MPI for Informatics, Max Planck Society;

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引用

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.

引用: https://hdl.handle.net/11858/00-001M-0000-000F-2126-1

要旨

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.