SpringerLink
Log in

PRM-KGED: paper recommender model using knowledge graph embedding and deep neural network

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

During the past decades, several academic paper recommendation systems were introduced in literature aiming to assist users in finding relevant papers close to their needs. In particular, those models have used users’ past preferences combined with papers’ side information to personalize their recommendations. Unfortunately, the majority of those models fail to utilize the implicit relationship between the crucial elements along with the semantic relations of the nodes in a Heterogeneous Information Network (HIN), which can further improve the accuracy of the models. In this paper, we propose a research article recommendation model that aims to tackle those issues by exploiting both textual and graph representations, simultaneously. The model employs SPECTER document embedding to learn context-preserving research article representations. In particular, it learns the features from a HIN, which is a knowledge graph of the node entities and their relationships. Then, an attention module strengthens the semantic feature extraction process and learns enhanced representations. The combined semantic and structural features are then provided as input to a Deep Neural Network (DNN) in order to learn high-level representations of query and candidate papers. We have evaluated our model against state-of-the-art models over two popular datasets. The results indicate a significant improvement in terms of MAP, recall, and MRR.

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
EUR 32.99 /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
Algorithm 1
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://www.aminer.cn/citation

  2. https://www.aminer.cn/citation

References

  1. Achakulvisut T, Acuna DE, Ruangrong T, et al (2016) Science concierge: a fast content-based recommendation system for scientific publications. ar**v:1604.01070

  2. Ali Z, Qi G, Kefalas P et al (2020) A graph-based taxonomy of citation recommendation models. Artif Intell Rev 53(7):5217–5260

    Article  Google Scholar 

  3. Ali Z, Qi G, Muhammad K et al (2020) Paper recommendation based on heterogeneous network embedding. Knowl Based Syst 210:106438

    Article  Google Scholar 

  4. Ali Z, Qi G, Muhammad K et al (2021) Global citation recommendation employing generative adversarial network. Expert Syst Appl 180:114888

    Article  Google Scholar 

  5. Ali Z, Qi G, Kefalas P, et al (2022a) Spr-smn: scientific paper recommendation employing specter with memory network. Scientometrics 1–23

  6. Ali Z, Qi G, Muhammad K et al (2022) Citation recommendation employing heterogeneous bibliographic network embedding. Neural Comput Appl 34(13):10229–10242

    Article  Google Scholar 

  7. Aliannejadi M, Rafailidis D, Crestani F (2019) A joint two-phase time-sensitive regularized collaborative ranking model for point of interest recommendation. IEEE Trans Knowl Data Eng 32(6):1050–1063

    Article  Google Scholar 

  8. Amami M, Pasi G, Stella F, et al (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, pp 200–210

  9. Amir N, Jabeen F, Ali Z et al (2023) On the current state of deep learning for news recommendation. Artif Intell Rev 56(2):1101–1144

    Article  Google Scholar 

  10. Auer S, Bizer C, Kobilarov G, et al (2007) Dbpedia: A nucleus for a web of open data. the semantic web. Lect Notes Comput Sci 4825(722):10–1007

  11. Bansal T, Belanger D, McCallum A (2016) Ask the GRU: multi-task learning for deep text recommendations. In: Sen S, Geyer W, Freyne J, et al (eds), Proceedings of the 10th ACM conference on recommender Systems, Boston, MA, USA, September 15-19, pp 107–114

  12. Beltagy I, Lo K, Cohan A (2019) Scibert: A pretrained language model for scientific text. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3615–3620

  13. Bollacker KD, Evans C, Paritosh PK, et al (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD international conference on management of data SIGMOD, Vancouver, BC, Canada, June 10-12, pp 1247–1250

  14. Bordes A, Usunier N, García-Durán A, et al (2013) Translating embeddings for modeling multi-relational data. In: Advances in Neural information processing systems 26: 27th annual conference on neural information processing systems. Proceedings of a meeting held December 5-8, Lake Tahoe, Nevada, United States, pp 2787–2795

  15. Cai X, Han J, Yang L (2018) Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, February 2-7, pp 5747–5754

  16. Cai X, Zheng Y, Yang L et al (2019) Bibliographic network representation based personalized citation recommendation. IEEE Access 7:457–467

    Article  Google Scholar 

  17. Cai X, Wang N, Yang L et al (2022) Global-local neighborhood based network representation for citation recommendation. Appl Intell 52(9):10098–10115

    Article  Google Scholar 

  18. Christoforidis G, Kefalas P, Papadopoulos AN et al (2021) RELINE: point-of-interest recommendations using multiple network embeddings. Knowl Inf Syst 63(4):791–817

    Article  Google Scholar 

  19. Cohan A, Feldman S, Beltagy I, et al (2020) SPECTER: document-level representation learning using citation-informed transformers. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020, pp 2270–2282

  20. Cui P, Wang X, Pei J et al (2019) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833–852

    Article  Google Scholar 

  21. Dai T, Yan W, Zhang K et al (2021) Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation. Expert Syst Appl 184:115359

    Article  Google Scholar 

  22. Dang D, Chen C, Li H et al (2021) Deep knowledge-aware framework for web service recommendation. J Supercomput 77(12):14280–14304

    Article  Google Scholar 

  23. Devlin J, Chang M, Lee K, et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, pp 4171–4186

  24. Ganguly S, Pudi V (2017) Paper2vec: Combining graph and text information for scientific paper representation. In: Jose JM, Hauff C, Altingövde IS, et al (eds) Advances in information retrieval - 39th European conference on IR research, ECIR 2017, Aberdeen, UK, April 8-13, pp 383–395

  25. Gazdar A, Hidri L (2020) A new similarity measure for collaborative filtering based recommender systems. Knowl Based Syst 188

  26. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78–94

    Article  Google Scholar 

  27. 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, San Francisco, CA, USA, August 13-17, 2016, pp 855–864

  28. Gu N, Gao Y, Hahnloser RHR (2022) Local citation recommendation with hierarchical-attention text encoder and scibert-based reranking. In: Advances in information retrieval - 44th European conference on IR Research ECIR, Stavanger, Norway, April 10-14, pp 274–288

  29. 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, Perth, Australia, April 3-7, 2017, pp 1267–1268

  30. Hu B, Fang Y, Shi C (2019) Adversarial learning on heterogeneous information networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD, Anchorage, AK, USA, August 4-8, pp 120–129

  31. Jeong C, Jang S, Park EL et al (2020) A context-aware citation recommendation model with BERT and graph convolutional networks. Scientometrics 124(3):1907–1922

    Article  Google Scholar 

  32. Ji G, He S, Xu L, et al (2015) Knowledge graph embedding via dynamic map** matrix. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian federation of natural language processing ACL, July 26-31, Bei**g, China, pp 687–696

  33. Ji S, Pan S, Cambria E et al (2022) A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494–514

    Article  MathSciNet  Google Scholar 

  34. Kefalas P, Symeonidis P, Manolopoulos Y (2018) Recommendations based on a heterogeneous spatio-temporal social network. World Wide Web 21(2):345–371

    Article  Google Scholar 

  35. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations ICLR, Toulon, France, April 24-26

  36. Kong X, Mao M, Wang W et al (2021) Voprec: vector representation learning of papers with text information and structural identity for recommendation. IEEE Trans Emerg Topics Comput 9(1):226–237

    Article  Google Scholar 

  37. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31th international conference on machine learning ICML, Bei**g, China, 21-26 June, pp 1188–1196

  38. Lin Y, Liu Z, Sun M, et al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI conference on artificial intelligence, January 25-30, Austin, Texas, USA, pp 2181–2187

  39. Liu D, Lian J, Wang S, et al (2020) KRED: knowledge-aware document representation for news recommendations. In: RecSys 2020: Fourteenth ACM conference on recommender systems, virtual event, Brazil, September 22-26, pp 200–209

  40. Mei X, Cai X, Xu S et al (2022) Mutually reinforced network embedding: an integrated approach to research paper recommendation. Expert Syst Appl 204:117616

    Article  Google Scholar 

  41. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining KDD, New York, NY, USA, August 24 - 27, pp 701–710

  42. Qiu T, Yu C, Zhong Y et al (2021) A scientific citation recommendation model integrating network and text representations. Scientometrics 126(11):9199–9221

  43. Rafailidis D, Kefalas P, Manolopoulos Y (2017) Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst Appl 74:11–18

    Article  Google Scholar 

  44. Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP, Hong Kong, China, November 3-7, pp 3980–3990

  45. Ribeiro LFR, Saverese PHP, Figueiredo DR (2017) struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada, August 13- 17, pp 385–394

  46. Sacenti JAP, Fileto R, Willrich R (2022) Knowledge graph summarization impacts on movie recommendations. J Intell Inf Syst 58(1):43–66

    Article  Google Scholar 

  47. Sun Z, Deng Z, Nie J, et al (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. In: 7th international conference on learning representations ICLR, New Orleans, LA, USA, May 6-9

  48. Tang J, Qu M, Wang M, et al (2015) LINE: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, WWW 2015, Florence, Italy, May 18-22, 2015, pp 1067–1077

  49. Trouillon T, Welbl J, Riedel S, et al (2016) Complex embeddings for simple link prediction. In: Proceedings of the 33nd international conference on machine learning ICML, New York City, NY, USA, June 19-24, pp 2071–2080

  50. Ullah I, Khusro S, Ahmad I (2021) Improving social book search using structure semantics, bibliographic descriptions and social metadata. Multimed Tools Appl 80(4):5131–5172

    Article  Google Scholar 

  51. Vrandecic D, Krö tzsch M, (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85

    Article  Google Scholar 

  52. Walek B, Fojtik V (2020) A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl 158:113452

    Article  Google Scholar 

  53. 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, San Diego, CA, USA, August 21-24, pp 448– 456

  54. Wang H, Li W (2015) Relational collaborative topic regression for recommender systems. IEEE Trans Knowl Data Eng 27(5):1343–1355

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  56. Wang L, Rao Y, Bian Q, et al (2020b) Content-based hybrid deep neural network citation recommendation method. In: Data Science - 6th International Conference of Pioneering Computer Scientists, Engineers and Educators ICPCSEE, Taiyuan, China, September 18-21, pp 3–20

  57. Wang M, Qiu L, Wang X (2021) A survey on knowledge graph embeddings for link prediction. Symmetry 13(3):485

    Article  Google Scholar 

  58. Wang Z, Zhang J, Feng J, et al (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence

  59. Wu Y, Zhao S (2021) Community answer generation based on knowledge graph. Inf Sci 545:132–152

    Article  Google Scholar 

  60. **a F, Liu H, Lee I et al (2016) Scientific article recommendation: exploiting common author relations and historical preferences. IEEE Trans Big Data 2(2):101–112

  61. Zhang Y, Wang J, Luo J (2020) Knowledge graph embedding based collaborative filtering. IEEE Access 8:134553– 134562

  62. Zhu Y, Lin Q, Lu H et al (2021) Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks. Knowl-Based Syst 215:106744

Download references

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Nan**g University of Posts and Telecommunications, Wenyuan Road, Nan**g, 210003, Jiangsu, China

    Nimbeshaho Thierry, Bing-Kun Bao & Ingabire Batamira Christ Chatelain

  2. College of Telecommunications and Information Engineering, Nan**g University of Posts and Telecommunications, **n Mofan Road, Nan**g, 210003, Jiangsu, China

    Bing-Kun Bao & Zhiyi Tan

  3. School of Computer Science and Engineering, Southeast University, Southeast Road, Nan**g, 211189, Jiangsu, China

    Zafar Ali

  4. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Pavlos Kefalas

Authors
  1. Nimbeshaho Thierry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing-Kun Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zafar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyi Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ingabire Batamira Christ Chatelain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pavlos Kefalas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nimbeshaho Thierry.

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

Thierry, N., Bao, BK., Ali, Z. et al. PRM-KGED: paper recommender model using knowledge graph embedding and deep neural network. Appl Intell 53, 30482–30496 (2023). https://doi.org/10.1007/s10489-023-05162-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05162-7

Keywords

Search

Navigation