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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
ACL, CIKM, EMNLP, ICDE, ICDM, KDD, SIGIR, VLDB, WSDM, WWW.
- 2.
References
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 421–430. ACM (2010)
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2333–2338. ACM (2013)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, X., Yu, Y., Guo, C., Sun, Y.: Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 121–130. ACM (2014)
Lu, Y., He, J., Shan, D., Yan, H.: Recommending citations with translation model. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2017–2020. ACM (2011)
Mahdabi, P., Crestani, F.: Query-driven mining of citation networks for patent citation retrieval and recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1659–1668. ACM (2014)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 101–110. ACM (2014)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd International Conference on World Wide Web. pp. 373–374. ACM (2014)
Strohman, T., Croft, W.B., Jensen, D.: Recommending citations for academic papers. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 705–706. ACM (2007)
Tang, J., Zhang, J.: A discriminative approach to topic-based citation recommendation. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 572–579. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01307-2_55
Tang, X., Wan, X., Zhang, X.: Cross-language context-aware citation recommendation in scientific articles. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 817–826. ACM (2014)
Wang, S., Lei, Z., Lee, W.C.: Exploring legal patent citations for patent valuation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1379–1388. ACM (2014)
Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 627–636. ACM (2014)
Liu, Y., Yan, R., Yan, H.: Guess what you will cite: personalized citation recommendation based on users’ preference. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 428–439. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45068-6_37
Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., Li, X.: Neural generative question answering. arXiv preprint arxiv:1512.01337 (2015)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-63564-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63563-7
Online ISBN: 978-3-319-63564-4
eBook Packages: Computer ScienceComputer Science (R0)