Skip to main content
SpringerLink
Log in

A personalized paper recommendation method based on knowledge graph and transformer encoder with a self-attention mechanism

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Paper recommendation with personalized methods helps researchers to track the latest academic trends and master cutting-edge academic trends efficiently. Meanwhile, the methods of previous paper recommendation suffer from three problems: data sparsity of content-based and collaborative filtering methods; Graph-based recommendations do not fully consider the personalized information of authors and their articles; Cold start based on deep learning. To overcome those difficulties, we propose a personalized paper recommendation method based on a knowledge graph and Transformer encoder (KGTE) with a self-attention mechanism. Firstly, we add auxiliary information (article title, publication year, citation times, and abstract) as attributes to the nodes of knowledge graph(KG), which contain author, digital object unique identifier(DOI) and keywords. Secondly, BERT is used to represent the semantic information features of the article and Transformer is introduced to fully integrate the feature context. After that, by using RippleNet, we traverse the knowledge graph, filter the user preference distribution and form a set of pre recommended nodes with multi_hop nodes. Finally, the prediction layer sorts the set and gets a Top_n paper recommendation. In the experiments on the DBLP and Aminer datasets, the precision value of KGTE improved by an average of 2.59% over the existing baseline methods DER and 4.23% improvement in NDCG.

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

Access this article

Log in via an institution

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The datasets DBLP and Aminer used in this study are available to the public under a Creative Commons license:

https://dblp.uni-trier.de/xml/

https://www.aminer.cn/

References

  1. Zhan Z, Xu B (2023) Analyzing review sentiments and product images by parallel deep nets for personalized recommendation. Inf Process Manag 60:103166

    Google Scholar 

  2. Gao L, Gan Y, Zhou B, Dong M (2020) Auser-knowledge crowdsourcing task assignment model and heuristic algorithm for expert knowledge recommendation systems. Eng Appl Artif Intell 96:103959

    Google Scholar 

  3. Gan M, Ma Y (2023) Mapping user interest into hyper-spherical space: a novel POI recommendation method. Inf Process Manag 60:103169

    Google Scholar 

  4. Gorgoglione M, Panniello U, Tuzhilin A (2019) Recommendation strategies in personalization applications. Inf Manag 56:103143

    Google Scholar 

  5. Liao G, Deng X, Wan C, Liu X (2022) Group event recommendation based on graph multi-head attention network combining explicit and implicit information. Inf Process Manag 59:102797

    Google Scholar 

  6. Dai T, Zhao J, Li D, Shen T, Zhao X, Pan S (2023) Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation. Expert Syst Appl 213:118841

    Google Scholar 

  7. Mei X, Cai X, Xu S, Li W, Pan S, Yang L (2022) Mutually reinforced network embedding: an integrated approach to research paper recommendation. Expert Syst Appl 204:117616

    Google Scholar 

  8. Shen T, Li J, Bouadjenek MR, Mai Z, Sanner S (2023) Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Inform Process Manag 60(1):103139

    Google Scholar 

  9. Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826

    Google Scholar 

  10. Chaudhuri A, Sarma M, Samanta D (2022) Share: designing multiple criteria-based personalized research paper recommendation system. Inf Sci 617:41–64

    Google Scholar 

  11. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and per-formance: a survey. Knowledge Based Syst 151:78–94

    Google Scholar 

  12. Zhang J, Gao M, Yu J, Yang L, Wang Z, Xiong Q (2021) Path-based reasoning over heterogeneous networks for recommendation via bidirectional modeling. Neurocomputing 461:438–449

    Google Scholar 

  13. Wu L, He X, Wang X, Zhang K, Wang M (2023) A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans Knowl Data Eng 35:4425–4445

    Google Scholar 

  14. Kong X, Mao M, Wang W, Liu J, Xu B (2021) VOPRec: vector representation learning of papers with text information and structural identity for recommendation. IEEE Trans Emerg Top Comput 9(1):226–237

    Google Scholar 

  15. Huang J, Han Z, Xu H, Liu H (2022) Adapted transformer network for news recommendation. Neurocomputing 469:119–129

    Google Scholar 

  16. Biswas PK, Liu S (2022) A hybrid recommender system for recommending smartphones to prospective customers. Expert Syst Appl 208:118058

    Google Scholar 

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

    Google Scholar 

  18. Stöckli DR, Khobzi H (2021) Recommendation systems and convergence of online reviews: the type of product network matters! Decis Support Syst 142:113475

    Google Scholar 

  19. Kuo RJ, Cheng H (2022) A content-based recommender system with consideration of repeat purchase behavior. Appl Soft Comput 127:109361

    Google Scholar 

  20. Trzebiński W, Marciniak B (2022) Recommender system information trustworthiness: the role of perceived ability to learn, self-extension, and intelligence cues. Comput Human Beh Reports 6:100193

    Google Scholar 

  21. Li X, Chen Y, Pettit B, De Rijke M (2019) Personalised reranking of paper recommendations using paper content and user behavior. ACM Trans Inf Syst 37:1–23

    Google Scholar 

  22. Guan Y, Wei Q, Chen G (2019) Deep learning based personalized recommendation with multi-view information integration. Decis Support Syst 118:58–69

    Google Scholar 

  23. Clemente J, Corral HY, De Pedro-Carracedo J, Bueno FJ (2022) A proposal for an adaptive recommender system based on competences and ontologies. Expert Syst Appl 208:118171

    Google Scholar 

  24. Chen Y, Qiu X, Ma C, Xu Y, Sun Y (2022) A recommender system fused with implicit social information through network representation learning. Comput Electr Eng 100:107897

    Google Scholar 

  25. Ren J, Long J, Xu Z (2019) Financial news recommendation based on graph embeddings. Decis Support Syst 125:113115

    Google Scholar 

  26. Kelleher C, Braswell A (2021) Introductory overview: recommendations for approaching scientific visualization with large environmental datasets. Environ Model Softw 143:105113

    Google Scholar 

  27. Li Y, Wang R, Nan G, Li D, Li M (2021) A personalized paper recommendation method considering diverse user preferences. Decis Support Syst 146:113546. https://doi.org/10.1016/j.dss.2021.113546

    Article  Google Scholar 

  28. Qian F, Zhu Y, Chen H, Chen J, Zhao S, Zhang Y (2022) Reduce unrelated knowledge through attribute collaborative signal for knowledge graph recommendation. Expert Syst Appl 201:117078

    Google Scholar 

  29. Da F, Kou G, Peng Y (2022) Deep learning based dual encoder retrieval model for citation recommendation. Technol Forecast Soc Chang 177:121545

    Google Scholar 

  30. Liu X, Wu K, Liu B, Qiu R (2023) HNERec: scientific collaborator recommendation model based on heterogeneous network embedding. Inf Process Manag 60:103253

    Google Scholar 

  31. Wang G, He X, Ishuga CI (2018) HAR-SI: a novel hybrid article recommendation approach integrating with social information in scientific social network. Knowledge Based Syst 148:85–99

    Google Scholar 

  32. Yuen M, King I, Leung K-S (2012) Task recommendation in crowdsourcing systems. Technol Soc

  33. Shen H, Xie J, Ao W, Cheng Y (2022) The continuity and citation impact of scientific collaboration with different gender composition. J Inform 16:101248

    Google Scholar 

  34. Ali Z, Qi G, Muhammad K, Kefalas P, Khusro S (2021) Global citation recommendation employing generative adversarial network. Expert Syst Appl 180:114888

    Google Scholar 

  35. Dai T, Zhu L, Wang Y, Carley KM (2020) Attentive stacked denoising autoencoder with bi-LSTM for personalized context-aware citation recommendation. IEEE/ACM Trans Audio, Speech, Language Process 28:553–568

    Google Scholar 

  36. Xu Y, Zhou D, Ma J (2019) Scholar-friend recommendation in online academic communities: an approach based on heterogeneous network. Decis Support Syst 119:1–13

    Google Scholar 

  37. Qiao Y, Sun L, Han J, Xiao C (2020) Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact. Appl Intell 52(2):2171–2188

    Google Scholar 

  38. Shakibian H, Charkari NM, Jalili S (2018) Multi-kernel one class link prediction in heterogeneous complex networks. Appl Intell 48:3411–3428

    Google Scholar 

  39. Zeb A, Saif S, Chen J, Zhang D (2022) Learning knowledge graph embeddings by deep relational roto-reflection. Knowledge Based Syst 252:109451

    Google Scholar 

  40. Wang P, Li J, Hou J (2021) S2san: a sentence-tosentence attention network for sentiment analysis of online reviews. Decis Support Syst 149:113603

    Google Scholar 

  41. Liu Z, Zhao Q, Zou L, Xu W, Min F (2022) A heuristic concept construction approach to collaborative recommendation. Int J Approx Reason 146:119–132

    MathSciNet  Google Scholar 

  42. Cai X, Zheng Y, Yang L, Dai T, Guo L (2019) Bibliographic network representation based personalized citation recommendation. IEEE Access 7:457–467

    Google Scholar 

  43. Kong X, Mao M, Wang W, Liu J, Xu B (2021) VOPRec: vector representation learning of papers with text information and structural identity for recommendation. IEEE Trans Emerg Top Comput 9:226–237

    Google Scholar 

  44. Dai T, Zhu L, Wang Y, Zhang H, Cai X, Zheng Y (2019) Joint model feature regression and topic learning for global citation recommendation. IEEE Access 7:1706–1720

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  47. Zhang S, Ma X, Wang Y, Zhou Y, Yu D (2022) An embedding and interactions learning approach for id feature in deep recommender system. Expert Syst Appl 210:118425

    Google Scholar 

  48. Devlin J, Chang M-W, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXivpreprint arXiv:1810.04805.

  49. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pages 417–426.

  50. Alshalabi H, Tiun S, Omar N, AbdulwahAbanaam E, Saif Y (2022) Bpr algorithm: new broken plural rules for an arabic stemmer. Egypt Inform J

  51. Dai Q, Wu X-M, Lu F, Li Q, Liu H, Zhang X, Wang D, Lin G, Yang K (2022) Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks. Pattern Recogn 128:108628

    Google Scholar 

Download references

Acknowledgements

The project was supported by the Ministry of Education Humanities and Social Sciences Foundation of China (20YJA870006). National Social Sciences Foundation of China (22BTQ021).

Author information

Authors and Affiliations

  1. University of Shanghai for Science and Technology, Library&The Department of Computer Science and Engineering, Shanghai, 200093, China

    Li Gao

  2. The Department of Computer Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yu Lan

  3. Shanghai Datong High School 200011, Shanghai, China

    Zhen Yu

  4. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jian-min Zhu

Authors
  1. Li Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian-min Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Li Gao: Conceptualization, Methodology, validation, Formal Analysis, Writing，riginal draft preparation, Software, Writing-Reviewing and Editing, Supervision, Funding Acquisition

Yu Lan: Experiment，Data curation, Writing- Original draft preparation, Software, Writing- Reviewing and Editing, Supervision

Zhen Yu: Experiment, Visualization, Investigation, Supervision

Jian-min Zhu: Writing- Reviewing and Editing, Supervision.

Corresponding author

Correspondence to Li Gao.

Ethics declarations

Competing interests

1. The authors have no relevant financial or non-financial interests to disclose.

2. The authors have no competing interests to declare that are relevant to the content of this article.

3. 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.

4. The authors have no financial or proprietary interests in any material discussed in this article.

Ethical statement

Ethical and informed consent for data used.

Research involving human participants and/or animals

Not involve.

Informed consent

Not involve.

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

Gao, L., Lan, Y., Yu, Z. et al. A personalized paper recommendation method based on knowledge graph and transformer encoder with a self-attention mechanism. Appl Intell 53, 29991–30008 (2023). https://doi.org/10.1007/s10489-023-05108-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05108-z

Keywords

Search

Navigation