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Citation recommendation using modified HITS algorithm

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Abstract

Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations.

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References

  1. Kunaver M, Požrl T (2017) Diversity in recommender systems-a survey. Knowl Based Syst 123:154–162

    Article  Google Scholar 

  2. Ali Z, Khusro S, Ullah I (2016) A hybrid book recommender system based on table of contents (toc) and association rule mining. In: Proceedings of the 10th International Conference on Informatics and Systems, pp 68–74

  3. Kleinberg JM (1999) Hubs, authorities, and communities. ACM Comput Surv (CSUR) 31:5

    Article  Google Scholar 

  4. Ali Z, Kefalas P, Muhammad K, Ali B, Imran M (2020) Deep learning in citation recommendation models survey. Expert Syst Appl 162:113790

    Article  Google Scholar 

  5. Amami M, Pasi G, Stella F, Faiz R (2016) An lda-based approach to scientific paper recommendation. In: Natural Language Processing and Information Systems: 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22–24, 2016, Proceedings 21, Springer, pp 200–210

  6. 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), Association for Computational Linguistics, pp 238–251. https://doi.org/10.18653/v1/N18-1022

  7. Wang C, Blei DM (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

  8. Bansal T, Belanger D, McCallum A (2016) Ask the gru: multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 107–114

  9. Wang H, Li W-J (2014) Relational collaborative topic regression for recommender systems. IEEE Trans Knowl Data Eng 27(5):1343–1355

    Article  Google Scholar 

  10. Cai X, Zheng Y, Yang L, Dai T, Guo L (2018) Bibliographic network representation based personalized citation recommendation. IEEE Access 7:457–467

    Article  Google Scholar 

  11. 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 

  12. 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, pp 421–430

  13. Zhang Y, Zhao R, Wang Y, Chen H, Mahmood A, Zaib M, Zhang WE, Sheng QZ (2022) Towards employing native information in citation function classification. Scientometrics, 1–21

  14. Zhang H, Shen F, Liu W, He X, Luan H, Chua T-S (2016) Discrete collaborative filtering. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 325–334

  15. Zhang Y, Wang H, Lian D, Tsang IW, Yin H, Yang G (2018) Discrete ranking-based matrix factorization with self-paced learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2758–2767

  16. Wang H, Lian D, Ge Y (2019) Binarized collaborative filtering with distilling graph convolutional networks. arXiv preprint arXiv:1906.01829

  17. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. The adaptive web: methods and strategies of web personalization, 325–341

  18. Khusro S, Ali Z, Ullah I (2016) Recommender systems: issues, challenges, and research opportunities. In: Information Science and Applications (ICISA) 2016, Springer, pp 1179–1189

  19. Son J, Kim SB (2018) Academic paper recommender system using multilevel simultaneous citation networks. Decis Support Syst 105:24–33

    Article  Google Scholar 

  20. Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. Recommender systems handbook, pp 73–105

  21. Tian G, Jing L (2013) Recommending scientific articles using bi-relational graph-based iterative rwr. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 399–402

  22. Chakraborty T, Modani N, Narayanam R, Nagar S (2015) Discern: a diversified citation recommendation system for scientific queries. In: 2015 IEEE 31st International Conference on Data Engineering, IEEE, pp 555–566

  23. 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 Trans Emerg Top Comput 9(1):226–237

    Article  Google Scholar 

  24. 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 

  25. Ali Z, Qi G, Muhammad K, Bhattacharyya S, Ullah I, Abro W (2022) Citation recommendation employing heterogeneous bibliographic network embedding. Neural Comput Appl 34(13):10229–10242

    Article  Google Scholar 

  26. Ali Z, Qi G, Muhammad K, Ali B, Abro WA (2020) Paper recommendation based on heterogeneous network embedding. Knowl Based Syst 210:106438

    Article  Google Scholar 

  27. Cheng G, Zhou P, Han J (2017) Duplex metric learning for image set classification. IEEE Trans Image Process 27(1):281–292

    Article  MathSciNet  MATH  Google Scholar 

  28. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, PMLR, pp 1188–1196

  29. Han J, Cheng G, Li Z, Zhang D (2017) A unified metric learning-based framework for co-saliency detection. IEEE Trans Circuits Syst Video Technol 28(10):2473–2483

    Article  Google Scholar 

  30. Pornprasit C, Liu X, Kertkeidkachorn N, Kim K-S, Noraset T, Tuarob S (2020) Convcn: A cnn-based citation network embedding algorithm towards citation recommendation. In: Proceedings of the ACM/IEEE joint conference on digital libraries in 2020, pp 433–436

  31. Xie Q, Zhu Y, Huang J, Du P, Nie J-Y (2021) Graph neural collaborative topic model for citation recommendation. ACM Trans Inf Syst (TOIS) 40(3):1–30

    Google Scholar 

  32. Kammari M et al (2023) Time-stamp based network evolution model for citation networks. Scientometrics 128(6):3723–3741

    Article  Google Scholar 

  33. Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 990–998

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Acknowledgements

We are grateful to the anonymous reviewers for their valuable comments and suggestions which helped in improving the quality of this manuscript. The first author acknowledges the financial assistance received from the University Grants Commission (UGC), Government of India in the form of a Junior Research Fellowship (JRF).

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  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Monachary Kammari & S. Durga Bhavani

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  1. Monachary Kammari
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  2. S. Durga Bhavani
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Correspondence to Monachary Kammari.

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

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Mathematics Subject Classification

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