FCMF: Federated collective matrix factorization for heterogeneous collaborative filtering
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Laplace, Gaussian, and Exponential mechanisms are commonly applied for noise addition to satisfy DP.
“Usually, Laplace [14, 15, 171, 172, 176, 177], Gaussian [101, 123, 125] & Exponential [126] mechanisms are commonly applied for noise addition to satisfy the DP.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [177]
Yang et al. [177] proposed the same hybrid approach by taking into account privacy breaches that might initiated due to collaborating different formats of user feedback data in FedRec architecture.
“Yang et al. [177] followed and proposed the same hybrid approach by taking into account privacy breaches that might initiated due to collaborating different formats of user feedback data in FedRec architecture.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [177]
Cited by
Sorted by citations
A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing
Lin, Z., Pan, W., Yang, Q., & Ming, Z. (2023) · ACM Transactions on Information Systems · doi:10.1145/3548456 · Cited by 44 · see citing works
Self-citationRoy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Yang, E., Huang, Y., Liang, F., Pan, W., & Ming, Z.
- Affiliations
- Shenzhen University (CN)
- Corresponding author
- Pan, Weike
- Ming, Zhong
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 220
- Pages
- 106946
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W3137510859
- Cited by
- 58
- References
- 0
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 10 · 2023: 13 · 2022: 14 · 2021: 5
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- federated learning, collective matrix factorization, privacy preservation, homomorphic encryption, differential privacy, rating data, recommender systems, gradient privacy
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.
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