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A content-sensitive citation representation approach for citation recommendation

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Abstract

Citation recommendation systems mainly help researchers find the lists of references that related to their interests effectively and automatically. The existing approaches face the issues of data sparsity and high-dimensional in large-scale bibliographic network representation, which hinder the citation recommendation performance. To address these problems, we proposed a Content-Sensitive citation representation approach for Citation Recommendation, named CSCR. Firstly, the Doc2vec model is used to generate a paper embedding according to paper content. Then, utilizing the similarity between the paper content embeddings to select the assumed neighbours of the target paper, append the auxiliary links between target paper and its new neighbours in the bibliographic network. Thirdly, distributed network representation method is implemented on appended bibliographic network to obtain the paper node embedding, which can learn interpretable lower dimension embedding for paper nodes. Finally, the embedding vectors of these papers can be used to conduct citation recommendation. Experimental results show that the proposed approach significantly outperforms other benchmark methods in Normalized Discounted Cumulative Gain (NDCG) and the positive rate (Recall).

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Notes

  1. http://clair.eecs.umich.edu/aan/downloads/.

References

  • Ali Z, Qi G, Kefalas P, Abro WA, Ali B (2020) A graph-based taxonomy of citation recommendation models. Artif Intell Rev 53:5217–5260

    Article  Google Scholar 

  • Amami M, Pasi G, Stella F, Faiz R (2016) An lda-based approach to scientific paper recommendation. International conference on applications of natural language to information systems. Springer, Berlin, pp 200–210

    Google Scholar 

  • Cai X, Han J, Li W, Zhang R, Pan S, Yang L (2018) A three-layered mutually reinforced model for personalized citation recommendation. IEEE Trans Neural Netw Learn Syst 29(12):6026–6037

    Article  Google Scholar 

  • Cao S, Lu W, Xu Q (2015) Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 891–900

  • Chen X, Hj Z, Zhao S, Chen J, Yp Z (2019) Citation recommendation based on citation tendency. Scientometrics 121(2):937–956

    Article  Google Scholar 

  • Dai T, Zhu L, Cai X, Pan S, Yuan S (2018) Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network. J Ambient Intell Hum Comput 9(4):957–975

    Article  Google Scholar 

  • Dai T, Zhu L, Wang Y, Carley KM (2019) Attentive stacked denoising autoencoder with bi-lstm for personalized context-aware citation recommendation. IEEE/ACM Trans Audio Speech Lang Process 28:553–568

    Article  Google Scholar 

  • Ding Y, Zhang G, Chambers T, Song M, Wang X, Zhai C (2014) Content-based citation analysis: the next generation of citation analysis. J Assoc Inform Sci Technol 65(9):1820–1833

    Article  Google Scholar 

  • Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 855–864

  • Guo L, Cai X, Hao F, Mu D, Fang C, Yang L (2017) Exploiting fine-grained co-authorship for personalized citation recommendation. IEEE Access 5:12714–12725

    Article  Google Scholar 

  • Guo L, Cai X, Qin H, Guo Y, Li F, Tian G (2019) Citation recommendation with a content-sensitive deepwalk based approach. In: 2019 International Conference on Data Mining Workshops (ICDMW), IEEE, pp 538–543

  • He Q, Pei J, Kifer D, Mitra P, Giles L (2010) Context-aware citation recommendation. In: Proceedings of the 19th International Conference on World Wide Web, ACM, pp 421–430

  • Hu Y, Xiong F, Pan S, Xiong X, Wang L, Chen H (2021) Bayesian personalized ranking based on multiple-layer neighborhoods. Inform Sci 542:156–176

    Article  MathSciNet  Google Scholar 

  • Ko Y (2012) A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1029–1030

  • Lau JH, Baldwin T (2016) An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368

  • Li Y, Wang S, Ma Y, Pan Q, Cambria E (2020) Popularity prediction on vacation rental websites. Neurocomputing 412:372–380

    Article  Google Scholar 

  • Liu Q, Chen E, Xiong H, Ding CH, Chen J (2011) Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Trans Syst Man Cybern 42(1):218–233

    Article  Google Scholar 

  • Liu H, Kong X, Bai X, Wang W, Bekele TM, Xia F (2015) Context-based collaborative filtering for citation recommendation. IEEE Access 3:1695–1703

    Article  Google Scholar 

  • Liu Z, Sun M, Lin Y, Xie R (2016) Knowledge representation learning: a review. J Comput Res Dev 53(2):247

    Google Scholar 

  • Liu H, Wang Y, Peng Q, Wu F, Gan L, Pan L, Jiao P (2020) Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374:77–85

    Article  Google Scholar 

  • Liu X, Yu Y, Guo C, Sun Y (2014) 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

  • Livne A, Gokuladas V, Teevan J, Dumais ST, Adar E (2014) Citesight: supporting contextual citation recommendation using differential search. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 807–816

  • Ma X, Wang R (2019) Personalized scientific paper recommendation based on heterogeneous graph representation. IEEE Access 7:79887–79894

    Article  Google Scholar 

  • Nallapati R, Cohen WW (2008) Link-plsa-lda: a new unsupervised model for topics and influence of blogs. In: Proceedings of the 12th International AAAI Conference on Web and Social Media, pp 84–92

  • Pan L, Dai X, Huang S, Chen J (2015) Academic paper recommendation based on heterogeneous graph. Chinese computational linguistics and natural language processing based on naturally annotated big data. Springer, Berlin, pp 381–392

    Chapter  Google Scholar 

  • Pan S, Hu R, Sf Fung, Long G, Jiang J, Zhang C (2019) Learning graph embedding with adversarial training methods. IEEE Trans Cyber 50(6):2475–2487

    Article  Google Scholar 

  • Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 701–710

  • Radev DR, Muthukrishnan P, Qazvinian V, Abu-Jbara A (2013) The ACL anthology network corpus. Lang Resour Eval 47(4):919–944

    Article  Google Scholar 

  • Ren X, Xea Y, Liu J (2014) Cluscite: effective citation recommendation by information network-based clustering. In: In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 821–830

  • Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp 1067–1077

  • Torres R, McNee SM, Abel M, Konstan JA, Riedl J (2004) Enhancing digital libraries with techlens. In: Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on, IEEE, pp 228–236

  • Totti LC, Mitra P, Ouzzani M, Zaki MJ (2016) A query-oriented approach for relevance in citation networks. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp 401–406

  • Wang J, Zhu L, Dai T, Wang Y (2020a) Deep memory network with bi-lstm for personalized context-aware citation recommendation. Neurocomputing 410:103–113

    Article  Google Scholar 

  • Wang W, Tang T, Xia F, Gong Z, Chen Z, Liu H (2020b) Collaborative filtering with network representation learning for citation recommendation. IEEE Trans Big Data. https://doi.org/10.1109/TBDATA.2020.3034976

    Article  Google Scholar 

  • Wei X, Croft WB (2006) Lda-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 178–185

  • West JD, Wesley-Smith I, Bergstrom CT (2016) A recommendation system based on hierarchical clustering of an article-level citation network. IEEE Trans Big Data 2(2):113–123

    Article  Google Scholar 

  • Xiong F, Shen W, Chen H, Pan S, Wang X, Yan Z (2019) Exploiting implicit influence from information propagation for social recommendation. IEEE Trans Cybern 50(10):4186–4199

    Article  Google Scholar 

  • Yang Z, Wu B, Zheng K, Wang X, Lei L (2016) A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4:3273–3287

    Article  Google Scholar 

  • Yang L, Zhang Z, Cai X, Guo L (2019) Citation recommendation as edge prediction in heterogeneous bibliographic network: a network representation approach. IEEE Access 7:23232–23239

    Article  Google Scholar 

  • Zhang W, Yoshida T, Tang X (2011) A comparative study of tf* idf, lsi and multi-words for text classification. Expert Syst Appl 38(3):2758–2765

    Article  Google Scholar 

  • Zhang D, Yin J, Zhu X, Zhang C (2018) Network representation learning: a survey. IEEE Trans Big Data 6(1):3–28

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2020JQ-214), and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (Program No. 840922).

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Authors and Affiliations

  1. School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266044, China

    Lantian Guo & Haohua Qin

  2. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Lantian Guo

  3. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiaoyan Cai & Sensen Guo

  4. Department of Computer Science, University of Exeter, Exeter, EX4 4QF, UK

    Fei Hao

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  1. Lantian Guo
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  2. Xiaoyan Cai
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  3. Haohua Qin
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  4. Fei Hao
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  5. Sensen Guo
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Correspondence to Sensen Guo.

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Guo, L., Cai, X., Qin, H. et al. A content-sensitive citation representation approach for citation recommendation. J Ambient Intell Human Comput 13, 3163–3174 (2022). https://doi.org/10.1007/s12652-021-03153-5

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