A Systematic Review of Citation Recommendation Over the Past Two Decades
Authors: 
Lap-Kei Lee, Yicong LiangAuthors Info & Claims
Pages 1 - 22
Abstract
A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.
References
[1]
Airoldi, E. M., Blei, D., Fienberg, S., & Xing, E. (2008). Mixed membership stochastic blockmodels. Advances in Neural Information Processing Systems, 21. 21701698.
[2]
Ali, Z., Kefalas, P., Muhammad, K., Ali, B., & Imran, M. (2020). Deep learning in citation recommendation models survey. Expert Systems with Applications, 162, 113790.
[3]
Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xia, F. (2019). Scientific paper recommendation: A survey. IEEE Access : Practical Innovations, Open Solutions, 7, 9324–9339.
[4]
BansalT.BelangerD.McCallumA. (2016). Ask the GRU: Multi-task learning for deep text recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, (pp. 107–114). ACM. 10.1145/2959100.2959180
[5]
Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305–338.
[6]
BethardS.JurafskyD. (2010). Who should I cite: Learning literature search models from citation behavior. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, (pp. 609–618). ACM. 10.1145/1871437.1871517
[7]
Bhagavatula, C., Feldman, S., Power, R., & Ammar, W. (2018). Content-based citation recommendation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 238-251). ACL.
[8]
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
[9]
Cai, X., Han, J., Li, W., Zhang, R., Pan, S., & Yang, L. (2018b). A three-layered mutually reinforced model for personalized citation recommendation. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6026–6037. 29993933.
[10]
CaiX.HanJ.YangL. (2018a). Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32. AAAI. 10.1609/aaai.v32i1.12037
[11]
Cai, X., Zheng, Y., Yang, L., Dai, T., & Guo, L. (2018c). Bibliographic network representation based personalized citation recommendation. IEEE Access : Practical Innovations, Open Solutions, 7, 457–467.
[12]
CarageaC.SilvescuA.MitraP.GilesC. L. (2013). Can’t see the forest for the trees? A citation recommendation system. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (pp. 111–114). IEEE. 10.1145/2467696.2467743
[13]
Chakraborty, T., Modani, N., Narayanam, R., & Nagar, S. (2015). Discern: A diversified citation recommendation system for scientific queries. 2015 IEEE 31st International Conference on Data Engineering, (pp. 555–566). IEEE.
[14]
Chang, J., & Blei, D. (2009). Relational topic models for document networks. Artificial Intelligence and Statistics, 81–88.
[15]
Chen, X., Zhao, H., Zhao, S., Chen, J., & Zhang, Y. (2019). Citation recommendation based on citation tendency. Scientometrics, 121(2), 937–956.
[16]
Dai, T., Zhu, L., Cai, X., Pan, S., & Yuan, S. (2018a). Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network. Journal of Ambient Intelligence and Humanized Computing, 9(4), 957–975.
[17]
Dai, T., Zhu, L., Wang, Y., Zhang, H., Cai, X., & Zheng, Y. (2018b). Joint model feature regression and topic learning for global citation recommendation. IEEE Access : Practical Innovations, Open Solutions, 7, 1706–1720.
[18]
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.
[19]
Färber, M., & Jatowt, A. (2020). Citation recommendation: Approaches and datasets. International Journal on Digital Libraries, 21(4), 375–405.
[20]
GalkeL.MaiF.VaglianoI.ScherpA. (2018). Multi-modal adversarial autoencoders for recommendations of citations and subject labels. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, (pp. 197–205). ACM. 10.1145/3209219.3209236
[21]
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, (vol. 2, pp. 2672–2680). ACM.
[22]
Gori, M., & Pucci, A. (2006). Research paper recommender systems: A random-walk based approach. 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), (pp. 778–781). IEEE.
[23]
Guo, L., Cai, X., Hao, F., Mu, D., Fang, C., & Yang, L. (2017). Exploiting fine-grained co-authorship for personalized citation recommendation. IEEE Access : Practical Innovations, Open Solutions, 5, 12714–12725.
[24]
Guo, L., Cai, X., Qin, H., Hao, F., & Guo, S. (2022). A content-sensitive citation representation approach for citation recommendation. Journal of Ambient Intelligence and Humanized Computing, 13(6), 1–12.
[25]
GuptaS.VarmaV. (2017). Scientific article recommendation by using distributed representations of text and graph. Proceedings of the 26th International Conference on World Wide Web Companion, (pp. 1267–1268). ACM. 10.1145/3041021.3053062
[26]
Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12), 2639–2664. 15516276.
[27]
HassanH. A. (2017). Personalized research paper recommendation using deep learning. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, (pp. 327–330). ACM. 10.1145/3079628.3079708
[28]
He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010). Context-aware citation recommendation. Proceedings of the 19th International Conference on World Wide Web, (pp. 421–430). ACM. https://doi.org/10.1145/1772690.1772734
[29]
JiangZ.LiuX.GaoL. (2014). Dynamic topic/citation influence modeling for chronological citation recommendation. Proceedings of the 5th International Workshop on Web-Scale Knowledge Representation Retrieval & Reasoning, (15–18). ACM. 10.1145/2663792.2663795
[30]
JiangZ.LiuX.GaoL. (2015). Chronological citation recommendation with information-need shifting. Proceedings of the 24th ACM International Conference on Information and Knowledge Management, (pp. 1291–1300). ACM.
[31]
Jiang, Z., Yin, Y., Gao, L., Lu, Y., & Liu, X. (2018). Cross-language citation recommendation via hierarchical representation learning on heterogeneous graph. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, (pp. 635–644). ACM.
[32]
Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. 5th International Conference on Learning Representations. arXiv preprint arXiv:1609.02907.
[33]
Kong, X., Mao, M., Wang, W., Liu, J., & Xu, B. (2018). VOPRec: Vector representation learning of papers with text information and structural identity for recommendation. IEEE Transactions on Emerging Topics in Computing, 9(1), 226–237.
[34]
Koren, Y., Rendle, S., & Bell, R. (2022). Advances in collaborative filtering. Recommender Systems Handbook, 91–142. 10.1007/978-1-0716-2197-4_3
[35]
LeQ.MikolovT. (2014). Distributed representations of sentences and documents. International Conference on Machine Learning, (pp. 1188–1196). ACM.
[36]
Li, X., Chen, Y., Pettit, B., & Rijke, M. D. (2019). Personalised reranking of paper recommendations using paper content and user behavior. [TOIS]. ACM Transactions on Information Systems, 37, 1–23.
[37]
LiuX.YuY.GuoC.SunY. (2014a). Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, (pp. 121–130). 10.1145/2661829.2661965
[38]
LiuX.YuY.GuoC.SunY.GaoL. (2014b). Full-text based context-rich heterogeneous network mining approach for citation recommendation. IEEE/ACM Joint Conference on Digital Libraries, (pp. 361–370). IEEE. 10.1109/JCDL.2014.6970191
[39]
LoK.WangL. L.NeumannM.KinneyR.WeldD. S. (2019). S2ORC: The semantic scholar open research corpus. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, (pp. 4969–4983). ACM.
[40]
Ma, S., Zhang, C., & Liu, X. (2020). A review of citation recommendation: From textual content to enriched context. Scientometrics, 122(3), 1445–1472.
[41]
McNeeS. M.AlbertI.CosleyD.GopalkrishnanP.LamS. K.RashidA. M.RiedlJ. (2002). On the recommending of citations for research papers. Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, (pp. 116–125). ACM. 10.1145/587078.587096
[42]
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26.
[43]
Mu, D., Guo, L., Cai, X., & Hao, F. (2017). Query-focused personalized citation recommendation with mutually reinforced ranking. IEEE Access : Practical Innovations, Open Solutions, 6, 3107–3119.
[44]
Nadkarni, A., & Hofmann, S. G. (2012). Why do people use Facebook? Personality and Individual Differences, 52(3), 243–249. 22544987.
[45]
NallapatiR. M.AhmedA.XingE. P.CohenW. W. (2008). Joint latent topic models for text and citations. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 542–550). ACM. 10.1145/1401890.1401957
[46]
PerozziB.Al-RfouR.SkienaS. (2014). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 701–710). ACM. 10.1145/2623330.2623732
[47]
RenX.LiuJ.YuX.KhandelwalU.GuQ.WangL.HanJ. (2014). Cluscite: Effective citation recommendation by information network-based clustering. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 821–830). ACM. 10.1145/2623330.2623630
[48]
RendleS. (2010). Factorization machines. 2010 IEEE International Conference on Data Mining, (pp. 995–1000). IEEE. 10.1109/ICDM.2010.127
[49]
RibeiroL. F.SavereseP. H.FigueiredoD. R. (2017). struc2vec: Learning node representations from structural identity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 385–394). ACM. 10.1145/3097983.3098061
[50]
StrohmanT.CroftW. B.JensenD. (2007). Recommending citations for academic papers. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 705–706). ACM.
[51]
Sun, Y., Han, J., Yan, X., Yu, P. S., & Wu, T. (2011). Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, 4(11), 992–1003.
[52]
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
[53]
WangJ.SongY.LeungT.RosenbergC.WangJ.PhilbinJ.WuY. (2014). Learning fine-grained image similarity with deep ranking. Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, (pp. 1386–1393). IEEE. 10.1109/CVPR.2014.180
[54]
Wu, Z., Gao, G., Bu, Z., & Cao, J. (2016). SIMPLE: A simplifying-ensembling framework for parallel community detection from large networks. Cluster Computing, 19(1), 211–221.
[55]
Yang, L., Zhang, Z., Cai, X., & Guo, L. (2019). Citation recommendation as edge prediction in heterogeneous bibliographic network: A network representation approach. IEEE Access : Practical Innovations, Open Solutions, 7, 23232–23239.
[56]
Zhang, Y., Yang, L., Cai, X., & Dai, H. (2018). A novel personalized citation recommendation approach based on GAN. In Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., & Raś, Z. (Eds.) Foundations of Intelligent Systems (pp. 268–278). ISMIS 2018. Lecture Notes in Computer Science, 11177.
[57]
Zhu, M. (2004). Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, 2.
Index Terms
- A Systematic Review of Citation Recommendation Over the Past Two Decades
Index terms have been assigned to the content through auto-classification.
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Published: 01 June 2023
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