Skip to main content
SpringerLink
Log in

CA-PDBPR: category-aware privacy preserving POI recommendation using decentralized Bayesian personalized ranking

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Point-of-interest (POI) recommendation has gained significant traction recently due to the rising trend of location-based networks. Traditional approaches rely on a centralized collection of user data. Concerning privacy protection, decentralized federated learning employs model training on each user’s device with nearby collaborative training techniques. However, existing decentralized federated recommendations suffer from two major problems: (1) Privacy risks: existing approaches expose geographical location or co-rated items information when constructing user neighborhoods. (2) Performance limitations: existing approaches adopt a simple model without incorporating auxiliary information. To solve these, we propose CA-PDBPR (category-aware privacy preserving POI recommendation using decentralized Bayesian personalized ranking) to address the above challenges. Specifically, we introduce a novel privacy-enhanced neighborhood creation method utilizing POI category preferences to calculate decentralized user similarity through secret sharing technology, ensuring a higher level of privacy. Moreover, we integrate POI category information with a refined Bayesian personalized ranking (BPR) loss function to enhance recommendation performance. Experimental evaluations conducted on real-world datasets validate the effectiveness of the CA-PDBPR model, demonstrating enhanced recommendation quality while minimizing data exposure compared with state-of-the-art alternatives.

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

Similar content being viewed by others

Data Availability

The dataset analysed during the current study is available in the [CA-PDBPR] repository, [https://github.com/gaoqinyun/CA-PDBPR/tree/main/data].

References

  1. Regulation P (2016) Regulation (eu) 2016/679 of the european parliament and of the council. Regulation (eu) 679:2016

    Google Scholar 

  2. Parameswaran R, Blough DM (2007) Privacy preserving collaborative filtering using data obfuscation. In: 2007 IEEE International Conference on Granular Computing (GRC 2007), pp. 380–380. IEEE

  3. Berkovsky S, Kuflik T, Ricci F (2012) The impact of data obfuscation on the accuracy of collaborative filtering. Expert Syst Appl 39(5):5033–5042

    Article  Google Scholar 

  4. Luo Z, Chen S, Li Y (2013) A distributed anonymization scheme for privacy preserving recommendation systems. In: 2013 IEEE 4th International Conference on Software Engineering and Service Science, pp. 491–494. IEEE

  5. Rubio JE, Alcaraz C, Lopez J (2017) Recommender system for privacy preserving solutions in smart metering. Pervasive Mob Comput 41:205–218

    Article  Google Scholar 

  6. Erkin Z, Veugen T, Toft T, Lagendijk RL (2012) Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Trans Inf Forensics Secur 7(3):1053–1066

    Article  Google Scholar 

  7. Huo Y, Chen B, Tang J, Zeng Y (2021) Privacy-preserving point-of-interest recommendation based on geographical and social influence. Inf Sci 543:202–218

    Article  MathSciNet  Google Scholar 

  8. Ran X, Wang Y, Zhang LY, Ma J (2022) A differentially private matrix factorization based on vector perturbation for recommender system. Neurocomputing 483:32–41

    Article  Google Scholar 

  9. Konečnỳ J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: Strategies for improving communication efficiency. arXiv:1610.05492

  10. Lin G, Liang F, Pan W, Ming Z (2020) Fedrec: Federated recommendation with explicit feedback. IEEE Intell Syst 36(5):21–30

    Article  Google Scholar 

  11. Zhu Z, Si S, Wang J, Xiao J (2022) Cali3f: Calibrated fast fair federated recommendation system. In: 2022 International Joint Conference on Neura Networks (IJCNN), pp. 1–8. IEEE

  12. Lin Z, Pan W, Ming Z (2021) Fr-fmss: Federated recommendation via fake marks and secret sharing. In: Proceedings of the 15th ACM Conference on Recommender Systems, pp. 668–673

  13. Yuan W, Yin H, Wu F, Zhang S, He T, Wang H (2023) Federated unlearning for on-device recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 393–401

  14. Yuan W, Yang C, Nguyen QVH, Cui L, He T, Yin H (2023) Interaction level membership inference attack against federated recommender systems. arXiv:2301.10964

  15. Zeng Q, Lv Z, Li C, Shi Y, Lin Z, Liu C, Song G (2023) Fedprols: federated learning for iot perception data prediction. Appl Intell 53(3):3563–3575

    Article  Google Scholar 

  16. Chen C, Liu Z, Zhao P, Zhou J, Li X (2018) Privacy preserving point-of-interest recommendation using decentralized matrix factorization. Proceedings of the AAAI Conference on Artificial Intelligence 32:257–264

    Google Scholar 

  17. Long J, Chen T, Nguyen QVH, Xu G, Zheng K, Yin H (2023) Model-agnostic decentralized collaborative learning for on-device poi recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 423–432

  18. Long J, Chen T, Nguyen QVH, Yin H (2023) Decentralized collaborative learning framework for next poi recommendation. ACM Transactions on Information Systems 41(3):1–25

    Article  Google Scholar 

  19. Zhang Y, Liu B, Qian J (2023) Fedpjf: federated contrastive learning for privacy-preserving person-job fit. Applied Intelligence 1–12

  20. Chai D, Wang L, Chen K, Yang Q (2020) Secure federated matrix factorization. IEEE Intell Syst 36(5):11–20

    Article  Google Scholar 

  21. Duriakova E, Tragos EZ, Smyth B, Hurley N, Peña FJ, Symeonidis P, Geraci J, Lawlor A (2019) Pdmfrec: a decentralised matrix factorisation with tunable user-centric privacy. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 457–461

  22. Duriakova E, Huáng W, Tragos E, Lawlor A, Smyth B, Geraci J, Hurley N (2021) An algorithmic framework for decentralised matrix factorisation. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part II, pp. 307–323. Springer

  23. Wang L, Zhao X, Lu Z, Wang L, Zhang S (2023) Enhancing privacy preservation and trustworthiness for decentralized federated learning. Inf Sci 628:449–468

    Article  Google Scholar 

  24. Yang X, Luo Y, Fu S, Xu M, Chen Y (2022) Dpmf: Decentralized probabilistic matrix factorization for privacy-preserving recommendation. Appl Sci 12(21):11118

    Article  Google Scholar 

  25. Zhou Y, Liu J, Wang JH, Wang J, Liu G, Wu D, Li C, Yu S (2022) Usst: A two-phase privacy-preserving framework for personalized recommendation with semi-distributed training. Inf Sci 606:688–701

    Article  Google Scholar 

  26. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295

  27. Cheng Z, Caverlee J, Lee K, Sui D (2011) Exploring millions of footprints in location sharing services. Proceedings of the International AAAI Conference on Web and Social Media 5:81–88

    Article  Google Scholar 

  28. Cheng C, Yang H, King I, Lyu M (2012) Fused matrix factorization with geographical and social influence in location-based social networks. Proceedings of the AAAI Conference on Artificial Intelligence 26:17–23

    Article  Google Scholar 

  29. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) Bpr: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618

  30. Zhao T, McAuley J, King I (2014) Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 261–270

  31. Hosseini S, Yin H, Zhou X, Sadiq S, Kangavari MR, Cheung NM (2019) Leveraging multi-aspect time-related influence in location recommendation. World Wide Web 22:1001–1028

    Article  Google Scholar 

  32. Xu C, Liu D, Mei X (2021) Exploring an efficient poi recommendation model based on user characteristics and spatial-temporal factors. Mathematics 9(21):2673

    Article  Google Scholar 

  33. Lam SK, Frankowski D, Riedl J (2006) Do you trust your recommendations? an exploration of security and privacy issues in recommender systems. In: International Conference on Emerging Trends in Information and Communication Security, pp. 14–29. Springer

  34. McSherry F, Mironov I (2009) Differentially private recommender systems: Building privacy into the netflix prize contenders. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627–636

  35. Riboni D, Bettini C (2012) Private context-aware recommendation of points of interest: An initial investigation. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 584–589. IEEE

  36. Liang F, Pan W, Ming Z (2021) Fedrec++: Lossless federated recommendation with explicit feedback. Proceedings of the AAAI Conference on Artificial Intelligence 35:4224–4231

    Article  Google Scholar 

  37. Ammad-Ud-Din M, Ivannikova E, Khan SA, Oyomno W, Fu Q, Tan KE, Flanagan A (2019) Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv:1901.09888

  38. Li D, Chen C, Lv Q, Shang L, Zhao Y, Lu T, Gu N (2016) An algorithm for efficient privacy-preserving item-based collaborative filtering. Futur Gener Comput Syst 55:311–320

  39. Chen C, Zhou J, Wu B, Fang W, Wang L, Qi Y, Zheng X (2020) Practical privacy preserving poi recommendation. ACM Transactions on Intelligent Systems and Technology (TIST) 11(5):1–20

    Article  Google Scholar 

  40. Perifanis V, Drosatos G, Stamatelatos G, Efraimidis PS (2023) Fedpoirec: Privacy-preserving federated poi recommendation with social influence. Inf Sci 623:767–790

    Article  Google Scholar 

  41. Ying S (2020) Shared mf: A privacy-preserving recommendation system. arXiv:2008.07759

  42. Mnih A, Salakhutdinov R (2007) Probabilistic matrix factorization. In advances in neural information processing systems. Advances in Neural Information Processing Systems

Download references

Acknowledgements

The work was supported in part by National Natural Science Foundation of China (Grants Nos. 12371515, 62176225 and 62171391).

Author information

Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, 361005, China

    Qinyun Gao, Bilian Chen & Langcai Cao

  2. Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making, Xiamen, 361005, China

    Qinyun Gao, Bilian Chen & Langcai Cao

  3. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, 350108, China

    Shenbao Yu

Authors
  1. Qinyun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shenbao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bilian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Langcai Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qinyun Gao: Conceptualization, Methodology, Software, Writing - Original Draft, Data curation, Writing - Review & Editing. Shenbao Yu: Methodology, Writing - Review & Editing. Bilian Chen: Methodology, Funding acquisition, Validation, Supervision, Writing - Review & Editing. Langcai Cao: Funding acquisition, Writing - Review & Editing.

Corresponding author

Correspondence to Bilian Chen.

Ethics declarations

competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical and informed consent for data used

We confirm that all data used in this study were obtained in accordance with ethical principles and informed consent procedures. All data used in this manuscript was obtained with the informed consent of participants.

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, Q., Yu, S., Chen, B. et al. CA-PDBPR: category-aware privacy preserving POI recommendation using decentralized Bayesian personalized ranking. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05426-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-05426-w

Keywords

Search

Navigation