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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Notes
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ACL, CIKM, EMNLP, ICDE, ICDM, KDD, SIGIR, VLDB, WSDM, WWW.
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Acknowledgments
This work has been partially supported by the 973 Program under Grant No. 2014CB340405 and National Natural Science Foundation of China under Grant No. U1536201.
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Yin, J., Li, X. (2017). Personalized Citation Recommendation via Convolutional Neural Networks. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_23
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