The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Feature selection by using privacy-preserving of recommendation systems based on collaborative filtering and mutual trust in social networks
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“However, to improve the privacy assurance of the perturbation approach, it can be implemented by integrating with other methods, such as k-anonymity [169], differential privacy [171].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [169]
In the citing paper
“Randomization approach, especially random perturbation, is foreseeable for the adversary due to adding a random number to create perturb data [169].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [169]
Citations
Cites
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.03.012 · Cited by 3,158
Kennedy, J. (2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8_630 · Cited by 3,068
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering
Strogatz, S. H., & Fox, R. F. (1995) · Physics Today · doi:10.1063/1.2807947 · Cited by 2,584
Strogatz, S. H. (2018) · CRC Press eBooks · doi:10.1201/9780429492563 · Cited by 2,247
Computing and applying trust in web-based social networks
Golbeck, J., & Hendler, J. (2005) · University Libraries (University of Maryland) · Cited by 833
Social Collaborative Filtering by Trust
Yang, B., Lei, Y., Liu, J., & Li, W. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2605085 · Cited by 671
Huber, P. J. (2011) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-642-04898-2_594 · Cited by 595
Chaos embedded particle swarm optimization algorithms
Alataş, B., Akın, E., & Özer, A. B. (2008) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2007.09.063 · Cited by 525
Collaborative filtering with privacy via factor analysis
Canny, J. (2002) · Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/564376.564419 · Cited by 508
Data Mining and Machine Learning in Cybersecurity
Dua, S., & Du, X. (2011) · Auerbach Publications eBooks · doi:10.1201/b10867 · Cited by 389
Research works printed but not linked: 4
- Banati H, Mehta M, Bajaj M (2014) Social behaviour based metrics to enhance collaborative filtering. Int J Comput Inf Syst Ind Manage Appl 6:217–226
- Li D et al (2016) An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Gener Comput Syst 55:311–320 · DOI
- Long Q, Hu Q (2010) Robust evaluation of binary collaborative recommendation under profile injection attack. In: 2010 IEEE international conference on progress in informatics and computing (PIC), vol 2. IEEE
- You H et al (2015) An improved collaborative filtering recommendation algorithm combining item clustering and Slope One scheme. In: Proceedings of the international multi-conference of engineers and computer scientists, vol 1
Cited by
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651 · Cited by 7
Metadata
- Authors
- Kashani, S. M. Z., & Hamidzadeh, J.
- Affiliations
- Sadjad University (IR)
- Corresponding author
- Hamidzadeh, Javad
- ORCID
- Hamidzadeh, Javad
- Venue
- Soft Computing
- Published
- 2019-12-12
- Volume
- 24
- Issue
- 15
- Pages
- 11425-11440
- ISSN
- 1432-7643 (ISSN-L)
- 1433-7479
- OpenAlex
- W2996607963
- Cited by
- 25
- References
- 65
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 5 · 2021: 2 · 2020: 3
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Soft Computing (publishedVersion)
- 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
- collaborative filtering, mutual trust, social networks, feature selection, user anonymity, fuzzy c-means clustering, particle swarm optimization
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
