Cited by 25Privacy-Preserving Technologies in DataRecommender Systems and TechniquesPrivacy, Security, and Data Protection

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

Kashani, Somayeh Moghaddam Zadeh, Hamidzadeh, Javad·2020·Soft Computing

DOI 10.1007/s00500-019-04605-z

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/s00500-019-04605-z

Cites

65 of 65 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Recommender systems survey
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.03.012 · Cited by 3,158
Particle Swarm Optimization
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
Nonlinear Dynamics and Chaos
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
Robust Statistics
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
Shilling attacks against recommender systems: a comprehensive survey
Gunes, I., Kaleli, C., Bilge, A., & Polat, H. (2014) · Artificial Intelligence Review · doi:10.1007/s10462-012-9364-9 · Cited by 325
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Izakian, H., & Abraham, A. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.07.112 · Cited by 273
Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing
Erkin, Z., Veugen, T., Toft, T., & Lagendijk, R. L. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190726 · Cited by 240
The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model
Wang, L., Wang, X., Peng, J., & Wang, J. (2019) · Tourism Management · doi:10.1016/j.tourman.2019.103961 · Cited by 220
Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems
Lam, S. K., Frankowski, D., & Riedl, J. (2006) · Lecture notes in computer science · doi:10.1007/11766155_2 · Cited by 188
A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
Bertino, E., Fovino, I. N., & Provenza, L. P. (2005) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0006-6 · Cited by 174
Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization
Filho, T. M. S., Pimentel, B. A., de Souza, R. M. C. R., & Oliveira, A. L. I. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.032 · Cited by 157
Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system
Cao, J., Wu, Z., Mao, B., & Zhang, Y. (2012) · World Wide Web · doi:10.1007/s11280-012-0164-6 · Cited by 128
An algorithm for efficient privacy-preserving item-based collaborative filtering
Li, D., Chen, C., Lv, Q., Shang, L., Zhao, Y., Lu, T., & Gu, N. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.11.003 · Cited by 121
Privacy-Preserving Collaborative Recommender Systems
Zhan, J., Hsieh, C.-L., Wang, I.-C., Hsu, T., Liau, C., & Wang, D. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2040275 · Cited by 120
Object-based classification of hyperspectral data using Random Forest algorithm
Amini, S., Homayouni, S., Safari, A., & Darvishsefat, A. A. (2018) · Geo-spatial Information Science · doi:10.1080/10095020.2017.1399674 · Cited by 119
A Fuzzy Decision Support Model With Sentiment Analysis for Items Comparison in e-Commerce: The Case Study of http://PConline.com
Ji, P., Zhang, H., & Wang, J. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2875163 · Cited by 110
A Linguistic Intuitionistic Cloud Decision Support Model with Sentiment Analysis for Product Selection in E-commerce
Liang, R., & Wang, J. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-019-00606-0 · Cited by 109
A k-anonymous approach to privacy preserving collaborative filtering
Casino, F., Domingo‐Ferrer, J., Patsakis, C., Puig, D., & Solanas, A. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.12.013 · Cited by 100
A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks
Guo, L., Zhang, C., & Fang, Y. M. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2014.2355824 · Cited by 96
An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system
Kaur, H., Kumar, N., & Batra, S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.017 · Cited by 89
Privacy-preserving collaborative recommendations based on random perturbations
Polatidis, N., Georgiadis, C. K., Pimenidis, E., & Mouratidis, H. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.018 · Cited by 80
Fuzzy c-ordered-means clustering
Łęski, J. M. (2014) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2014.12.007 · Cited by 70
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Bilge, A., & Polat, H. (2013) · Information Processing & Management · doi:10.1016/j.ipm.2013.02.004 · Cited by 66
TBRS: A trust based recommendation scheme for vehicular CPS network
Liang, W., Long, J., Weng, T., Chen, X., Li, K., & Zomaya, A. Y. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.09.002 · Cited by 66
Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers
Yi, X., & Zhang, Y. (2008) · Information Systems · doi:10.1016/j.is.2008.11.001 · Cited by 60
ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks
Ma, X., Ma, J.-F., Li, H., Jiang, Q., & Gao, S. (2018) · Future generations computer systems · doi:10.1016/j.future.2017.09.060 · Cited by 54
A novel image segmentation approach for wood plate surface defect classification through convex optimization
Chang, Z., Cao, J., & Zhang, Y. (2018) · Journal of Forestry Research · doi:10.1007/s11676-017-0572-7 · Cited by 54
Big data clustering with varied density based on MapReduce
Heidari, S., Alborzi, M., Radfar, R., Afsharkazemi, M. A., & Ghatari, A. R. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0236-x · Cited by 49
Automatic support vector data description
Sadeghi, R., & Hamidzadeh, J. (2016) · Soft Computing · doi:10.1007/s00500-016-2317-5 · Cited by 47
Self-citation
When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System
Liu, X., Liu, A., Zhang, X., Li, Z., Liu, G., Zhao, L., & Zhou, X. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-55753-3_36 · Cited by 42
2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)
(2017) · doi:10.1109/icccbda40479.2017 · Cited by 42
Privacy-preserving Collaborative Filtering based on Randomized Perturbation Techniques and Secure Multiparty Computation
Gong, S. (2011) · International Journal of Advancements in Computing Technology · doi:10.4156/ijact.vol3.issue4.10 · Cited by 39
APPLET: a privacy-preserving framework for location-aware recommender system
Ma, X., Li, H., Ma, J., Jiang, Q. G., Gao, S., Xi, N., & Lu, D. (2016) · Science China Information Sciences · doi:10.1007/s11432-015-0981-4 · Cited by 38
Enforcing Differential Privacy for Shared Collaborative Filtering
Li, J., Yang, J., Zhao, Y. D., Liu, B., Zhou, M., Bi, J., & Wang, Q. (2016) · IEEE Access · doi:10.1109/access.2016.2600258 · Cited by 36
Weighted support vector data description based on chaotic bat algorithm
Hamidzadeh, J., Sadeghi, R., & Namaei, N. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.07.038 · Cited by 36
Self-citation
A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection
Dou, K., Guo, B., & Kuang, L. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4352-3 · Cited by 35
Chaos
Alligood, K. T., SAUER, T. D., & Yorke, J. A. (1997) · Textbooks in mathematical sciences · doi:10.1007/978-3-642-59281-2_3 · Cited by 29
A survey on statistical disclosure control and micro‐aggregation techniques for secure statistical databases
Fayyoumi, E., & Oommen, B. J. (2010) · Software Practice and Experience · doi:10.1002/spe.992 · Cited by 24
Belief-based chaotic algorithm for support vector data description
Hamidzadeh, J., & Namaei, N. (2018) · Soft Computing · doi:10.1007/s00500-018-3083-3 · Cited by 23
Self-citation
A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
Han, J., Hui-qun, Y., Yu, J., & Ting-ting, C. (2008) · International Symposium on Electronic Commerce and Security · doi:10.1109/isecs.2008.92 · Cited by 21
A Genetic Approach to Multivariate Microaggregation for Database Privacy
Martínez-Ballesté, A., Solanas, A., Domingo‐Ferrer, J., & Mateo‐Sanz, J. M. (2007) · doi:10.1109/icdew.2007.4400989 · Cited by 20
Private collaborative filtering under untrusted recommender server
Xiong, P., Zhang, L., Zhu, T., Li, G., & Zhou, W. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.05.077 · Cited by 20
p-Probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation
Rebollo‐Monedero, D., Forné, J., Soriano, M., & Allepuz, J. P. (2016) · Information Sciences · doi:10.1016/j.ins.2016.12.002 · Cited by 18
A Novel Way of Assigning Software Bug Priority Using Supervised Classification on Clustered Bugs Data
Goyal, N., Aggarwal, N., & Dutta, M. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-11218-3_44 · Cited by 15
Data Security in Recommendation System Using Homomorphic Encryption
Soni, K., & Panchal, G. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-63673-3_37 · Cited by 15
Privacy preserving collaborative filtering for KNN attack resisting
Zhu, T., Li, G., Pan, L., Ren, Y., & Zhou, W. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0196-2 · Cited by 13
Revisiting the logistic map: A closer look at the dynamics of a classic chaotic population model with ecologically realistic spatial structure and dispersal
Storch, L. S., Pringle, J. M., Alexander, K. E., & Jones, D. O. (2016) · Theoretical Population Biology · doi:10.1016/j.tpb.2016.11.004 · Cited by 13
A Flexible Privacy-Preserving Framework for Singular Value Decomposition Under Internet of Things Environment
Chen, S., Lu, R., & Zhang, J. (2017) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-59171-1_3 · Cited by 12
A novel recommendation algorithm frame for tourist Spots based on multi-clustering bipartite graphs
Xiong, H., Zhou, Y. J., Hu, C., Wei, X., & Li, L. (2017) · IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) · doi:10.1109/icccbda.2017.7951924 · Cited by 12
Detection of Profile-injection Attacks in Recommender Systems Using Outlier Analysis
Chakraborty, P., & Karforma, S. (2013) · Procedia Technology · doi:10.1016/j.protcy.2013.12.444 · Cited by 11
Average Shilling Attack against Trust-Based Recommender Systems
Zhang, F. (2009) · International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII) · doi:10.1109/iciii.2009.601 · Cited by 11
Analysis of Love-Hate Shilling Attack Against E-commerce Recommender System
Zhang, F. (2010) · doi:10.1109/isme.2010.116 · Cited by 10
Privacy-preserving Collaborative Filtering Using Randomized Response
Kikuchi, H., & Mochizuki, A. (2013) · Journal of Information Processing · doi:10.2197/ipsjjip.21.617 · Cited by 9
A Hybrid Item-based Recommendation Algorithm against Segment Attack in Collaborative Filtering Systems
Li, C., & Luo, Z. (2011) · International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII) · doi:10.1109/iciii.2011.242 · Cited by 9
Multi-Layer Perceptron Classifier and Paillier Encryption Scheme for Friend Recommendation System
Patil, K., & Jadhav, N. (2017) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2017.8463832 · Cited by 9
A Method for Privacy-preserving Collaborative Filtering Recommendations
Georgiadis, C. K., Pimenidis, E., Mouratidis, H., & Polatidis, N. (2020) · University of Brighton Repository (University of Brighton) · doi:10.3217/jucs-023-02-0146 · Cited by 8
AN IMPROVED PRIVACY-PRESERVING COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM
Zhang, J., Zhu, J., & Zhang, N. (2014) · Pacific Asia Conference on Information Systems · Cited by 7
A trustworthy online recommendation system based on social connections in a privacy-preserving manner
Chiou, S.-Y. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3534-8 · Cited by 6
An end-to-end encrypted domain proximity recommendation system using secret sharing homomorphic cryptography
Shieh, J.-R. (2015) · International Carnahan Conference on Security Technology · doi:10.1109/ccst.2015.7389682 · Cited by 3

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

A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651 · Cited by 7

See all 25

Authors: Kashani, S. M. Z., & Hamidzadeh, J.
Affiliations: Sadjad University (IR)
Corresponding author: Hamidzadeh, Javad
ORCID: Hamidzadeh, Javad
Venue: Soft Computing
Publisher: Springer Science and Business Media LLC
Published: 2019-12-12
Volume: 24
Issue: 15
Pages: 11425-11440
ISSN: 1432-7643 (ISSN-L)
1433-7479
DOI: 10.1007/s00500-019-04605-z
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 ›

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

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.

Feature selection by using privacy-preserving of recommendation systems based on collaborative filtering and mutual trust in social networks

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Metadata

Record

About these checks

Data sources