CoChat
Sign Up
Cited by 10Recommender Systems and TechniquesPrivacy-Preserving Technologies in DataInformation Retrieval and Search Behavior

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

FedNRM: A Federal Personalized News Recommendation Model Achieving User Privacy Protection

Open access
Yu, Shoujian, Jie, Zhenchi, Wu, Guowen, Zhang, Hong, Shen, Shigen·2023·Intelligent Automation & Soft Computing
DOI 10.32604/iasc.2023.039911
Open this paper in CoChat — free accountOpen PDF

Abstract

In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candi... | Find, read and cite all the research you need on Tech Science Press

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.32604/iasc.2023.039911

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Compared with traditional cloud services, the new access control scheme proposed in the current research has solved known authorization management problems, but it can only be used for limited cloud platforms.

    “Compared with traditional cloud services, the new access control scheme proposed in the current research has solved known authorization management problems, but it can only be used for limited cloud platforms [132].”

    From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [132]

    The Introduction and Section 2.3 discuss federated news recommendation and privacy protection, not an access-control scheme for cloud platforms. Searches for authorization management and limited cloud-platform use found no passage addressing either point; page numbers were not available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 45

  1. Li, 2011, Personalized news recommendation: A review and an experimental investigation, Computer Science & Technology, 26, 754, 10.1007/s11390-011-0175-2 · DOI
  2. Wang, 2023, An accuracy-enhanced group recommendation approach based on DEMATEL, Pattern Recognition Letters, 167, 171, 10.1016/j.patrec.2023.02.008 · DOI
  3. Wu, 2023, Popularity-aware and diverse web APIs’ recommendation based on correlation graph, IEEE Transactions on Computational Social Systems, 10, 771, 10.1109/TCSS.2022.3168595 · DOI
  4. Feng, 2023, One-dimensional VGGNet for high-dimensional data, Applied Soft Computing, 135, 110035, 10.1016/j.asoc.2023.110035 · DOI
  5. Cui, 2021, Pre-training with whole word masking for Chinese bert, IEEE/ACM Transactions on Audio, Speech, & Language Processing, 29, 3504, 10.1109/TASLP.2021.3124365 · DOI
  6. Suzuki, 2023, Constructing and analyzing domain-specific language model for financial text mining, Information Processing & Management, 60, 103194, 10.1016/j.ipm.2022.103194 · DOI
  7. Tian, 2022, FedBERT: When federated learning meets pre-training, ACM Transactions on Intelligent Systems and Technology (TIST, 13, 1, 10.1145/3510033 · DOI
  8. Rothe, 2020, Leveraging pre-trained checkpoints for sequence generation tasks, Transactions of the Association for Computational Linguistics, 8, 264, 10.1162/tacl_a_00313 · DOI
  9. Zang, 2022, MPAN: Multi-parallel attention network for session-based recommendation, Neurocomputing, 471, 230, 10.1016/j.neucom.2021.11.030 · DOI
  10. Ravi, 2019, SECRECSY: A secure framework for enhanced privacy-preserving location recommendations in cloud environment, Wireless Personal Communications, 108, 1869, 10.1007/s11277-019-06500-0 · DOI
  11. Shen, 2022, Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes, Digital Communications and Networks, 10.1016/j.dcan.2022.05.004 · DOI
  12. Sun, 2022, Stimulating trust cooperation in edge services: An evolutionary tripartite game, Engineering Applications of Artificial Intelligence, 116, 105465, 10.1016/j.engappai.2022.105465 · DOI
  13. Qu, 2022, FedTwin: Blockchain-enabled adaptive asynchronous federated learning for digital twin networks, IEEE Network, 36, 183, 10.1109/MNET.105.2100620 · DOI
  14. Cui, 2022, Security and privacy-enhanced federated learning for anomaly detection in IoT infrastructures, IEEE Transactions on Industrial Informatics, 18, 3492, 10.1109/TII.2021.3107783 · DOI · Paper page
  15. Hu, 2021, Differentially private locality sensitive hashing based federated recommender system, Concurrency and Computation: Practice & Experience, 10.1002/cpe.6233 · DOI
  16. Sun, 2022, Fed-DFE: A decentralized function encryption-based privacy-preserving scheme for federated learning, CMC-Computers Materials & Continua, 71, 1867, 10.32604/cmc.2022.022290 · DOI
  17. T. Qi, F. Z. Wu, C. H. Wu, Y. F. Huang and X. Xie, “Privacy-preserving news recommendation model learning,” CoRR, abs/2003.09592, 2020. [Online]. Available: https://arxiv.org/abs/2003.09592 · DOI
  18. Wang, 2023, Blockchain-aided network resource orchestration in intelligent internet of things, IEEE Internet of Things Journal, 10, 6151, 10.1109/JIOT.2022.3222911 · DOI
  19. Zhang, 2022, RKD-VNE: Virtual network embedding algorithm assisted by resource knowledge description and deep reinforcement learning in IIoT scenario, Future Generation Computer Systems, 135, 426, 10.1016/j.future.2022.05.008 · DOI
  20. Tran, 2022, CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation, World Wide Web-Internet & Web Information Systems, 10.1007/s11280-022-01059-6 · DOI
  21. Zhang, 2021, TLSAN: Time-aware long-and short-term attention network for next-item recommendation, Neurocomputing, 441, 179, 10.1016/j.neucom.2021.02.015 · DOI
  22. Hu, 2020, Graph neural news recommendation with long-term and short-term interest modeling, Information Processing & Management, 57, 102142, 10.1016/j.ipm.2019.102142 · DOI
  23. Thilakarathne, 2022, Federated learning for privacy-preserved medical internet of things, Intelligent Automation & Soft Computing, 33, 157, 10.32604/iasc.2022.023763 · DOI
  24. M. Ammad, E. Ivannikova, S. AKhan, W. Oyomno, Q. Fu et al., “Federated collaborative filtering for privacy-preserving personalized recommendation system,” CoRR, abs/1901.09888, 2019. [Online]. Available: https://arxiv.org/abs/1901.09888 · arXiv
  25. Lin, 2021, FedRec: Federated recommendation with explicit feedback, IEEE Intelligent Systems, 36, 21, 10.1109/MIS.2020.3017205 · DOI
  26. C. H. Wu, F. Z. Wu, Y. Cao, Y. F. Huang and X. Xie, “FedGNN: Federated graph neural network for privacy-preserving recommendation,” CoRR, abs/2102.04925, 2021. [Online]. Available: https://arxiv.org/abs/2102.04925 · arXiv
  27. Zhu, 2020, Privacy-preserving weighted federated learning within the secret sharing framework, IEEE Access, 8, 198275, 10.1109/ACCESS.2020.3034602 · DOI
  28. Nosouhi, 2023, UCoin: An efficient privacy preserving scheme for cryptocurrencies, IEEE Transactions on Dependable and Secure Computing, 20, 242, 10.1109/TDSC.2021.3130952 · DOI · Paper page
  29. Liu, 2020, Privacy preserving distributed data mining based on secure multi-party computation, Computer Communications, 153, 208, 10.1016/j.comcom.2020.02.014 · DOI
  30. Xu, 2022, Secure multi-party quantum summation based on quantum homomorphic encryption, Intelligent Automation & Soft Computing, 34, 531, 10.32604/iasc.2022.028264 · DOI
  31. Yin, 2020, Improving sentence representations via component focusing, Applied Sciences, 10, 958, 10.3390/app10030958 · DOI
  32. Guo, 2019, Low-rank and locality constrained self-attention for sequence modeling, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27, 2213, 10.1109/TASLP.2019.2944078 · DOI
  33. Kong, 2022, Time-aware missing healthcare data prediction based on ARIMA model, IEEE/ACM Transactions on Computational Biology and Bioinformatics
  34. Cheng, 2021, Sentiment analysis using multi-head attention capsules with multi-channel CNN and bidirectional GRU, IEEE Access, 9, 60383, 10.1109/ACCESS.2021.3073988 · DOI
  35. Ni, 2020, An effective recommendation model based on deep representation learning, Information Sciences, 542, 324, 10.1016/j.ins.2020.07.038 · DOI
  36. Shen, 2022, Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination, Journal of Information Security and Applications, 66, 103140, 10.1016/j.jisa.2022.103140 · DOI
  37. Shin, 2018, Privacy enhanced matrix factorization for recommendation with local differential privacy, IEEE Transactions on Knowledge and Data Engineering, 30, 1770, 10.1109/TKDE.2018.2805356 · DOI
  38. Sun, 2020, Lazily aggregated quantized gradient innovation for communication-efficient federated learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 2031, 10.1109/TPAMI.2020.3033286 · DOI · Paper page
  39. Zhang, 2023, Self-powered electronic skin for remote human-machine synchronization, ACS Applied Electronic Materials, 5, 498, 10.1021/acsaelm.2c01476 · DOI
  40. Guo, 2022, Online estimation of SOH for lithium-ion battery based on SSA-elman neural network, Protection and Control of Modern Power Systems, 7, 40, 10.1186/s41601-022-00261-y · DOI
  41. Zhang, 2023, Electrochemical impedance spectroscopy: A new chapter in the fast and accurate estimation of the state of health for lithium-ion batteries, Energies, 16, 1599, 10.3390/en16041599 · DOI
  42. Ma, 2023, Aging mechanism and models of supercapacitors: A review, Technologies, 11, 38, 10.3390/technologies11020038 · DOI
  43. Yang, 2020, Personalized news recommendation based on the text and image integration, Computers Materials & Continua, 64, 557, 10.32604/cmc.2020.09907 · DOI
  44. Tang, 2022, News recommendation with latent topic distribution and long and short-term user representations, Data Analysis and Knowledge Discovery, 6, 52
  45. Binbusayyis, 2022, Deep embedded fuzzy clustering model for collaborative filtering recommender system, Intelligent Automation & Soft Computing, 33, 501, 10.32604/iasc.2022.022239 · DOI

Cited by

Sorted by citations

Metadata

Authors
Yu, S., Jie, Z., Wu, G., Zhang, H., & Shen, S.
Affiliations
  • Donghua University (CN)
  • Huzhou Normal University (CN)
ORCID
Venue
Intelligent Automation & Soft Computing
Publisher
Tech Science Press
Published
2023
Volume
37
Issue
2
Pages
1729-1751
ISSN
1079-8587 (ISSN-L)
DOI
10.32604/iasc.2023.039911
OpenAlex
W4381956047
Cited by
10
References
2
Citations by year
2025: 6 · 2024: 2 · 2023: 2
FWCI
2.35
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
Intelligent Automation & Soft Computing (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
news recommendation, personalized recommendation, user privacy protection, federated learning, personalized news, privacy-preserving recommendation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review