Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Personalized global citation recommendation with diversification awareness

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Citation recommendation helps researchers perform reference searching more efficiently. Traditional methods often focus separately on diversification and personalization, each with unique advantages and limitations. In this study, we propose a new citation recommendation paradigm, personalized global citation recommendation with diversification awareness (PGCR-DA), which integrates the two approaches to generate more relevant candidate citations. Our work involved two major tasks. The first task involves generating a pool of diversified candidate citations for each target paper, by using the Random Walk with Restart on a constructed heterogeneous graph to identify the first relevant citation. The remaining diversified candidates are returned by using the Maximal Marginal Relevance model, where diversified citations are obtained based on a two-dimensional, i.e., the semantic space and publication date of the paper, diversification strategy. The second task focuses on personalization, where the ranking list obtained in the first task is reranked by modeling fine-grained and dynamic user preferences, informed by the analysis of both the textual and entity space from the users’ previous publications. Preliminary experiments on the AAN and DBLP datasets verify our hypothesis that diversification and personalization can be effectively integrated through our approach. The results further demonstrate that PGCR-DA outperforms the competitive global citation recommendation methods with respect to a series of metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. Available at: https://github.com/allenai/scibert/.

  2. Available at: http://clair.eecs.umich.edu/aan/index.php.

  3. Available at: https://www.aminer.cn/citation.

  4. Available at: http://gibbslda.sourceforge.net.

  5. Available at: https://github.com/boudinfl/pke.

References

  • Ali, Z., Kefalas, P., Muhammad, K., Ali, B., & Imran, M. (2020). Deep learning in citation recommendation models survey. Expert Systems with Applications, 162, 113790. https://doi.org/10.1016/j.eswa.2020.113790

    Article  Google Scholar 

  • Ali, Z., Qi, G. L., Muhammad, K., Bhattacharyya, S., Ullah, I., & Abro, W. (2021a). Citation recommendation employing heterogeneous bibliographic network embedding. In Neural Computing and Applications, 34, 10229–10242. https://doi.org/10.1007/s00521-021-06135-y

    Article  Google Scholar 

  • Ali, Z., Qi, G. L., Muhammad, K., Kefalas, P., & Khusro, S. (2021b). Global citation recommendation employing generative adversarial network. Expert Systems with Applications, 180, 114888. https://doi.org/10.1016/j.eswa.2021.114888

    Article  Google Scholar 

  • Ali, Z., Ullah, I., Khan, A., Jan, A. U., & Muhammad, K. (2021c). An overview and evaluation of citation recommendation models. Scientometrics, 126, 4083–4119. https://doi.org/10.1007/s11192-021-03909-y

    Article  Google Scholar 

  • Anand, A., Chakraborty, T., & Das, A. (2017). FairScholar: Balancing relevance and diversity for scientific paper recommendation. In European Conference on Information Retrieval, 10193, 753–757. Lecture Notes in Computer Science. Springer: Cham. https://doi.org/10.1007/978-3-319-56608-5_76

  • Ayala-Gomez, F., Daroczy, B., Benczur, A., Mathioudakis, M., & Gionis, A. (2018). Global citation recommendation using knowledge graphs. Journal of Intelligent and Fuzzy Systems, 34(5), 3089–3100. https://doi.org/10.3233/JIFS-169493

    Article  Google Scholar 

  • Cai, X. Y., Han, J. W., Li, W. J., Zhang, R. X., Pan, S. R., & Yang, L. B. (2018). A three-layered mutually reinforced model for personalized citation recommendation. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6026–6037. https://doi.org/10.1109/TNNLS.2018.2817245

    Article  Google Scholar 

  • Cai, X. Y., Zheng, Y., Yang, L. B., Dai, T., & Guo, L. T. (2019). Bibliographic network representation based personalized citation recommendation. IEEE Access, 7, 457–467. https://doi.org/10.1109/ACCESS.2018.2885507

    Article  Google Scholar 

  • Chakraborty, T., Modani, N., Narayanam, R., & Nagar, S. (2015). Discern: A diversified citation recommendation system for scientific queries. In IEEE 31st international conference on data engineering (pp. 555–566). https://doi.org/10.1109/ICDE.2015.7113314.

  • Chen, W. Y., Cai, F., Chen, H. H., & Rijke, M. D. (2020). Personalized query suggestion diversification in information retrieval. Frontiers of Computer Science, 14(3), 143602. https://doi.org/10.1007/s11704-018-7283-x

    Article  Google Scholar 

  • Chen, X., Zhao, H. J., Zhao, S., Chen, J., & Zhang, Y. P. (2019). Citation recommendation based on citation tendency. Scientometrics, 121(2), 937–956. https://doi.org/10.1007/s11192-019-03225-6

    Article  Google Scholar 

  • Cheng, P.Z., Wang, S.Q., Ma, J., Sun, J.K., & Xiong, H. (2017). Learning to recommend accurate and diverse items. In Proceedings of the 26th international conference on World Wide Web (pp. 183–192). https://doi.org/10.1145/3038912.3052585

  • Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., & Mackinnon, I. (2008). Novelty and diversity in information retrieval evaluation. In Proceedings of the 31st annual international ACM SIGIR conference on research and the development in information retrieval (pp. 659–666). https://doi.org/10.1145/1390334.1390446

  • Dinh, T. N., Pham, P., Nguyen, G. L., & Vo, B. (2024). Enhancing local citation recommendation with recurrent highway networks and SciBERT-based embedding. Expert Systems with Application, 243, 122911. https://doi.org/10.1016/j.eswa.2023

    Article  Google Scholar 

  • Ebesu, T., & Fang, Y. (2017). Neural citation network for context-aware citation recommendation. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 1093–1096). https://doi.org/10.1145/3077136.3080730

  • Gori, M., & Pucci, A. (2006). Research paper recommender systems: A random-walk based approach. In Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence (pp. 778–781). https://doi.org/10.1109/WI.2006.149

  • Gündoan, E., & Kaya, M. (2022). A novel hybrid paper recommendation system using deep learning. Scientometrics, 127(7), 3837–3855. https://doi.org/10.1007/s11192-022-04420-8

    Article  Google Scholar 

  • Guo, L. T., Cai, X. Y., Hao, F., Mu, D. J., Fang, C. J., & Yang, L. B. (2017). Exploiting fine-grained co-authorship for personalized citation recommendation. IEEE Access, 5, 12714–12725. https://doi.org/10.1109/ACCESS.2017.2721934

    Article  Google Scholar 

  • Guo, L. T., Cai, X. Y., Qin, H. H., Hao, F., & Guo, S. S. (2022). A content-sensitive citation representation approach for citation recommendation. Journal of Ambient Intelligence and Humanized Computing, 13, 3163–3174. https://doi.org/10.1007/s12652-021-03153-5

    Article  Google Scholar 

  • Gupta, S., & Varma,V. (2017). Scientific article recommendation by using distributed representations of text and graph. In Proceedings of the 26th international conference on World Wide Web companion (pp. 1267–1268). https://doi.org/10.1145/3041021.3053062

  • Huang, W.Y., Kataria, S., Caragea, C., Mitra, P., Giles, L.C., & Rokach, L. (2012). Recommending citations: Translating papers into references. In Proceedings of the 21st ACM international conference on information and knowledge management (pp. 1910–1914). https://doi.org/10.1145/2396761.2398542

  • Jebari, C., Herrera-Viedma, E., & Cobo, M. J. (2023). Context-aware citation recommendation of scientific papers: comparative study, gaps and trends. Scientometrics, 128, 4243–4268. https://doi.org/10.1007/s11192-023-04773-8

    Article  Google Scholar 

  • Jeong, C., Jang, S., Park, E., & Choi, S. (2020). A context-aware citation recommendation model with BERT and graph convolutional networks. Scientometrics, 124, 1907–1922. https://doi.org/10.1007/s11192-020-03561-y

    Article  Google Scholar 

  • Jiang, Z.R., Liu, X.Z., & Gao, L.C. (2014). Dynamic topic/citation influence modeling for chronological citation recommendation. In Proceedings of the 5th international workshop on web-scale knowledge representation retrieval and reasoning (pp. 15–18). ACM. https://doi.org/10.1145/2663792.2663795

  • Kammari, M., & Bhavani, S. D. (2023). Citation recommendation using modified HITS algorithm. Computing. https://doi.org/10.1007/s00607-023-01213-6

    Article  Google Scholar 

  • Kieu, B. T., Unanue, I. J., Pham, S. B., Phan, H. X., & Piccardi, M. (2021). NeuSub: A neural submodular approach for citation recommendation. IEEE Access, 9, 148459–148468. https://doi.org/10.1109/ACCESS.2021.3120727

    Article  Google Scholar 

  • Kucuktunc, O., Saule, E., Kaya, K., & Çatalyürek, Ü. V. (2012). Diversifying citation recommendations. Acm Transactions on Intelligent Systems & Technology, 5(4), 1–21. https://doi.org/10.1145/2668106

    Article  Google Scholar 

  • Li, X., Chen, Y., Pettit, B., & Rijke, M. D. (2019). Personalised reranking of paper recommendations using paper content and user behavior. ACM Transactions on Information Systems, 37(3), 1–23. https://doi.org/10.1145/3312528

    Article  Google Scholar 

  • Liang, S.S., Ren, Z.C., & Rijke, M.D. (2014). Personalized search result diversification via structured learning. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 751–760). https://doi.org/10.1145/2623330.2623650

  • Liu, Y.N, Yan, R., & Yan, H.F. (2013). Guess what you will cite: Personalized citation recommendation based on users’ preference. In: R. E. Banchs, F. Silvestri, T. Y. Liu, M. Zhang, S. Gao, and J. Lang (eds.), Information retrieval technology, 8281. AIRS 2013. Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-642-45068-6_37

  • Liu, H. F., Kong, X. J., Bai, X. M., Wang, W., Bekele, T. M., & Xia, F. (2015). Context-based collaborative filtering for citation recommendation. IEEE Access, 3, 1695–1703. https://doi.org/10.1109/ACCESS.2015.2481320

    Article  Google Scholar 

  • Lu, Y. H., Yuan, M., Liu, J. X., & Chen, M. H. (2023). Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion. Scientometrics, 128, 1367–1393. https://doi.org/10.1007/s11192-022-04566-5

    Article  Google Scholar 

  • Ma, S. T., Zhang, H., Zhang, C. Z., & Liu, X. Z. (2021). Chronological citation recommendation with time preference. Scientometrics, 126, 2991–3010. https://doi.org/10.1007/s11192-021-03878-2

    Article  Google Scholar 

  • Meng, F.Q., Gao, D.H., Li, W.J., Sun, X., & Hou, Y.X. (2013). A unified graph model for personalized query-oriented reference paper recommendation. In Proceedings of the 22nd ACM international conference on information & knowledge management (pp. 1509–1512). https://doi.org/10.1145/2505515.2507831

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, 2, 3111–3119. https://doi.org/10.48550/arXiv.1310.4546

    Article  Google Scholar 

  • Mu, D. J., Guo, L. T., Cai, X. Y., & Hao, F. (2018). Query-focused personalized citation recommendation with mutually reinforced ranking. IEEE Access, 6, 3107–3119. https://doi.org/10.1109/ACCESS.2017.2787179

    Article  Google Scholar 

  • Nallapati, R.M., Ahmed, A., Xing, E.P., & Cohen, W.W. (2008). Joint latent topic models for text and citations. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 542–550). https://doi.org/10.1145/1401890.1401957

  • Pornprasit, C., Liu, X., Kiattipadungkul, P., Kertkeidkachorn, N., Kim, K.-S., Noraset, T., Hassan, S.-U., & Tuarob, S. (2022). Enhancing citation recommendation using citation network embedding. Scientometrics, 127(1), 233–264. https://doi.org/10.1007/s11192-021-04196-3

    Article  Google Scholar 

  • Qi, H., Jian, P., Kifer, D., Mitra, P., & Giles, L. (2010). Context-aware citation recommendation. In Proceedings of the 19th international conference on World Wide Web (pp. 421–430). https://doi.org/10.1145/1772690.1772734

  • Radev, D. R., Muthukrishnan, P., Qazvinian, V., & Abu-Jbara, A. (2013). The ACL anthology network corpus. Language Resources and Evaluation, 47(4), 919–944. https://doi.org/10.1007/s10579-012-9211-2

    Article  Google Scholar 

  • Radlinski,F., & Dumais,S. (2006) .Improving personalized web search using result diversification. In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 691–692). https://doi.org/10.1145/1148170.1148320

  • Strohman, T., Croft, W.B., & Jensen, D. (2007). 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). https://doi.org/10.1145/1277741.1277868

  • Sugiyama, K. & Kan, M.-Y. (2013). Exploiting potential citation papers in scholarly paper recommendation. In Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries (pp. 153–162). https://doi.org/10.1145/2467696.2467701

  • Vallet, D., & Castells, P. (2012). Personalized diversification of search results. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval (pp. 841–850). https://doi.org/10.1145/2348283.2348396

  • Wang, C., & Blei, D.M. (2011). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 448–456). https://doi.org/10.1145/2020408.2020480

  • Wang, X.J., Qi, J.Z., Ramamohanarao, K., Sun, Y., Li, B., & Zhang, R. (2018). A joint optimization approach for personalized recommendation diversification. In: D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, and L. Rashidi (eds.), Advances in knowledge discovery and data mining, 10939 (pp. 597–609). PAKDD 2018. Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-319-93040-447

  • Wang, S.J., Hu, L., Wang, Y., He, X.N., Sheng, Q.Z., Orgun, M.A., Cao, L.B., Ricci, F., & Yu, P.S. (2021). Graph learning based recommender systems: A review. In Proceedings of the 30th international joint conference on artificial intelligence (IJCAI 2021 Survey Track) (pp. 1–9). https://doi.org/10.48550/arXiv.2105.06339

  • Wu,Q., Liu, Y., Miao, C.Y., Zhao, B.Q., Zhao, Y., & Guan, L. (2019). PD-GAN: Adversarial learning for personalized diversity-promoting recommendation. In Proceedings of the 28th international joint conference on artificial intelligence (pp. 3870–3876). https://doi.org/10.24963/ijcai.2019/537

  • Wu, D., Yang, R. X., & Shen, C. (2021). Sentiment word co-occurrence and knowledge pair feature extraction based LDA short text clustering algorithm. Journal of Intelligent Information Systems, 56(1), 1–23. https://doi.org/10.1007/s10844-020-00597-7

    Article  Google Scholar 

  • Xie, Q. Q., Zhu, Y. T., Huang, J. M., Du, P., & Nie, J. Y. (2022). Graph neural collaborative topic model for citation recommendation. ACM Transactions on Information Systems, 40(48), 1–30. https://doi.org/10.1145/3473973

    Article  Google Scholar 

  • Yang, Y.H., Chen, H.H., Lu,W., & Ayala, B.R. (2018). Diversifying citation contexts in academic literature for knowledge recommendation. In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries (pp. 397–398). https://doi.org/10.1145/3197026.3203904

  • Yang, L. B., Zhang, Z. Q., Cai, X. Y., & Dai, T. (2019). Attention-based personalized encoder-decoder model for local citation recommendation. Computational Intelligence and Neuroscience, 2019, 1–7. https://doi.org/10.1155/2019/1232581

    Article  Google Scholar 

  • Zhang, Y., Yang, L., Cai, X., & Dai, H. (2018). A novel personalized citation recommendation approach based on GAN. In International symposium on methodologies for intelligent systems (pp. 268–278). https://doi.org/10.1007/978-3-030-01851-1_26

  • Zhang, J. Z., & Zhu, L. P. (2022). Citation recommendation using semantic representation of cited papers’ relations and content. Expert Systems with Applications, 187, 115826. https://doi.org/10.1016/j.eswa.2021.115826

    Article  Google Scholar 

  • Zhang, Y., & Ma, Q. (2020). Dual attention model for citation recommendation. Proceedings of the 28th International Conference on Computational Linguistics, 48, 403–470. https://doi.org/10.1162/coli_a_00438

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the anonymous reviewers for their valuable comments and suggestions which helped in improving the quality of this manuscript.

Funding

This work is supported by The National Social Science Fund of China, 21BTQ072.

Author information

Authors and Affiliations

  1. School of Public Administration, Sichuan University, Sichuan, China

    Xiaojuan Zhang, Shuqi Song & Yuping Xiong

Authors
  1. Xiaojuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuqi Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuping Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaojuan Zhang.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Song, S. & Xiong, Y. Personalized global citation recommendation with diversification awareness. Scientometrics 129, 3625–3657 (2024). https://doi.org/10.1007/s11192-024-05057-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-024-05057-5

Keywords

Search

Navigation