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Combining Node Identifier Features and Community Priors for Within-Network Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Personalized Citation Recommendation via Convolutional Neural Networks

Authors : Jun Yin, Xiaoming Li

Published in: Web and Big Data

Publisher: Springer International Publishing

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Abstract

Automatic citation recommendation based on citation context, together with consideration of users’ preference and writing patterns is an emerging research topic. In this paper, we propose a novel personalized convolutional neural networks (p-CNN) discriminatively trained by maximizing the conditional likelihood of the cited documents given a citation context. The proposed model not only nicely represents the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also includes authorship information. It includes each paper’s author into our neural network’s input layer and thus can generate semantic content features and representative author features simultaneously. The results show that the proposed model can effectively captures salient representations and hence significantly outperforms several baseline methods in citation recommendation task in terms of recall and Mean Average Precision rates.

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Footnotes
1
ACL, CIKM, EMNLP, ICDE, ICDM, KDD, SIGIR, VLDB, WSDM, WWW.
 
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Metadata
Title
Personalized Citation Recommendation via Convolutional Neural Networks
Authors
Jun Yin
Xiaoming Li
Copyright Year
2017
Book
Web and Big Data

Print ISBN: 978-3-319-63563-7

Electronic ISBN: 978-3-319-63564-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63564-4_23

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