PUB
  • Home
  • Sources
  • Search
  • About

A News Recommendation Method for User Privacy Protection

Yiqing Chen
International Journal of Computer Science and Information Technology, (2024), Vol.2, No.3, pp.25-36
Published: May 28, 2024
DOI: 10.62051/ijcsit.v2n3.04
PDF: Download Full Text PDF
Abstract

News recommendations play an important role in daily life, but there is a problem of privacy leakage. To address this problem, a news recommendation method for user privacy protection is proposed. It combines news recommendation with a federated learning framework to ensure accurate recommendations while protecting user privacy. To be specific, the method contains three key components: 1) It combines federated learning to ensure that user data is processed locally and not leaked to third parties, thus achieving privacy protection. 2) It fully considers the long-term and short-term interests of users and learns their short-term interests using the GRU model based on the item features of interaction, which provides a more comprehensive perspective on user modelling. 3) We design a score prediction method that fuses linear and nonlinear features to better capture the interaction between users and news and improve the accuracy of recommendation. The experimental validation on two public news datasets, Adressa and MIND, demonstrates that the method can still achieve efficient news recommendation without compromising user privacy.

Keywords: News Recommendation; Privacy protection; Federated Learning
APA Citation: Yiqing Chen (2024). A News Recommendation Method for User Privacy Protection. International Journal of Computer Science and Information Technology, 2(3), 25-36. https://doi.org/10.62051/ijcsit.v2n3.04

References

  1. ZHENG G, ZHANG F, ZHENG Z, et al. DRN: A deep reinforcement learning framework for news recommendation [C]// proceedings of the Proceedings of the 2018 world wide web conference, 2018: 167-176.
  2. OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users [C]// proceedings of the Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017: 1933-1942.
  3. WANG H, ZHANG F, XIE X, et al. DKN: Deep knowledge-aware network for news recommendation [C]// proceedings of the Proceedings of the 2018 world wide web conference, 2018: 1835-1844.
  4. WU C, WU F, GE S, et al. Neural news recommendation with multi-head self-attention [C]// proceedings of the Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), 2019: 6389-6394.
  5. WU C, WU F, QI T, et al. Empowering news recommendation with pre-trained language models [C]// proceedings of the Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 1652-1656.
  6. QI T, WU F, WU C, et al. Privacy-preserving news recommendation model learning [J]. arXiv preprint arXiv:200309592, 2020.
  7. YI J, WU F, WU C, et al. Efficient-FedRec: Efficient federated learning framework for privacy-preserving news recommendation [J]. arXiv preprint arXiv:210905446, 2021.
  8. IANA A, GLAVAS G, PAULHEIM H. Simplifying content-based neural news recommendation: On user modeling and training objectives [C]// proceedings of the Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023: 2384-2388.
  9. MOOKIAH L, EBERLE W, MONDAL M. Personalized news recommendation using graph-based approach [J]. Intelligent Data Analysis, 2018, 22(4): 881-909.
  10. GERSHMAN A, WOLFE T, FINK E, et al. News personalization using support vector machines [J]. 2011.
  11. CAPELLE M, FRASINCAR F, MOERLAND M, et al. Semantics-based news recommendation [C]// proceedings of the Proceedings of the 2nd international conference on web intelligence, mining and semantics, 2012: 1-9.
  12. ZHANG L, LIU P, GULLA J A. A deep joint network for session-based news recommendations with contextual augmentation [M]. Proceedings of the 29th on Hypertext and Social Media. 2018: 201-209.
  13. WU C, WU F, AN M, et al. Neural news recommendation with topic-aware news representation [C]// proceedings of the Proceedings of the 57th Annual meeting of the association for computational linguistics, 2019: 1154-1159.
  14. YANG B, LIU D, SUZUMURA T, et al. Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations [C]// proceedings of the Proceedings of the 17th ACM Conference on Recommender Systems, 2023: 24-34.
  15. WU C, WU F, AN M, et al. Neural news recommendation with attentive multi-view learning [J]. arXiv preprint arXiv:190705576, 2019.
  16. LIU D, LIAN J, WANG S, et al. KRED: Knowledge-aware document representation for news recommendations [C]// proceedings of the Proceedings of the 14th ACM Conference on Recommender Systems, 2020: 200-209.
  17. ZHANG Q, JIA Q, WANG C, et al. Amm: Attentive multi-field matching for news recommendation [C]// proceedings of the Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 1588-1592.
  18. WU C, WU F, QI T, et al. Mm-rec: Visiolinguistic model empowered multimodal news recommendation [C]// proceedings of the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 2560-2564.
  19. WU C, WU F, QI T, et al. Is News Recommendation a Sequential Recommendation Task? [C]// proceedings of the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 2382-2386.
  20. MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data [C]// proceedings of the Artificial intelligence and statistics, 2017: PMLR: 1273-1282.
  21. AMMAD-UD-DIN M, IVANNIKOVA E, KHAN S A, et al. Federated collaborative filtering for privacy-preserving personalized recommendation system [J]. arXiv preprint arXiv:190109888, 2019.
  22. WU C, WU F, CAO Y, et al. Fedgnn: Federated graph neural network for privacy-preserving recommendation [J]. arXiv preprint arXiv:210204925, 2021.
  23. CHEN G, ZHANG X, SU Y, et al. Win-win: a privacy-preserving federated framework for dual-target cross-domain recommendation [C]// proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, 2023: 4149-4156.
  24. LI L, LIN F, XIAHOU J, et al. Federated low-rank tensor projections for sequential recommendation [J]. Knowledge-Based Systems, 2022, 255: 109483.
  25. LIU R, CAO Y, WANG Y, et al. PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation [C]// proceedings of the Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023: 4539-4548.
  26. YI J, WU F, ZHU B, et al. UA-FedRec: untargeted attack on federated news recommendation [C]// proceedings of the Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023: 5428-5438.
  27. KENTON J D M-W C, TOUTANOVA L K. Bert: Pre-training of deep bidirectional transformers for language understanding [C]// proceedings of the Proceedings of naacL-HLT, 2019: 2.
  28. KUMMERVOLD P E, DE LA ROSA J, WETJEN F, et al. Operationalizing a national digital library: The case for a Norwegian transformer model [J]. arXiv preprint arXiv:210409617, 2021.
  29. LIAN J, ZHANG F, XIE X, et al. Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach [C]// proceedings of the IJCAI, 2018: 3805-3811.
© 2026 PUB ยท Seek truth with rigour