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Cited by 29Recommender Systems and TechniquesSpam and Phishing DetectionPrivacy-Preserving Technologies in Data

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RETRACTED ARTICLE: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

Rani, Shalli, Kaur, Manpreet, Kumar, Munish, Ravi, Vinayakumar, Ghosh, Uttam, Mohanty, Jnyana Ranjan·2023·Soft Computing
DOI 10.1007/s00500-021-05586-8
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Research works printed but not linked: 28

  1. Aivazoglou M, Roussos AO, Margaris D, Vassilakis C, Ioannidis S, Polakis J, Spiliotopoulos D (2020) A fine-grained social network recommender system. Soc Netw Anal Min 10(1):8 · DOI
  2. Batmaz Z, Yilmazel B, Kaleli C (2020) Shilling attack detection in binary data: a classification approach. J Ambient Intell Humaniz Comput 11:2601–2611 · DOI
  3. Bilge A, Ozdemir Z, Polat H (2014) A novel shilling attack detection method. Procedia Comput Sci 31:165–174 · DOI
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  5. Bozanta A, Kutlu B (2019) HybRecSys: Content-based contextual hybrid venue recommender system. J Inf Sci 45(2):212–226 · DOI
  6. Cai H, Zhang F (2019) Detecting shilling attacks in recommender systems based on analysis of user rating behavior. Knowl-Based Syst 177:22–43 · DOI
  7. Chen K, Chan PP, Zhang F, Li Q (2019) Shilling attack based on item popularity and rated item correlation against collaborative filtering. Int J Mach Learn Cybern 10(7):1833–1845 · DOI
  8. De Lucia MJ, & Cotton C (2020) A network security classifier defense: against adversarial machine learning attacks. In: Proceedings of the 2nd ACM workshop on wireless security and machine learning, pp 67–73 · DOI
  9. Deldjoo Y, Di Noia T, Merra FA (2021) A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks. ACM Comput Surv. https://doi.org/10.1145/3439729 · DOI
  10. García-Sánchez F, Colomo-Palacios R, Valencia-García R (2020) A social-semantic recommender system for advertisements. Inf Process Manage 57(2):102153 · DOI
  11. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–71 · DOI
  12. Gunes I, Kaleli C, Bilge A, Polat H (2014) Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 42(4):767–799 · DOI
  13. Kaur P, Goel S (2016) Shilling attack models in the recommender system. In: 2016 International conference on inventive computation technologies (ICICT), Vol 2, pp 1–5, IEEE. · DOI
  14. Kumar PV, Reddy VR (2014) A survey on recommender systems (RSS) and its applications. Int J Innov Res Comput Commun Eng 2(8):5254–5260
  15. Logesh R, Subramaniyaswamy V, Vijayakumar V, Li X (2019) Efficient user profiling based intelligent travel recommender system for individual and group of users. Mobile Netw Appl 24(3):1018–1033 · DOI
  16. Mao Y, Huang W, Zhong H, Wang Y, Qin H, Guo Y, Huang D (2020) Detecting quantum attacks: a machine learning based defense strategy for practical continuous-variable quantum key distribution. New J Phys 22(8):083073 · DOI
  17. Missaoui S, Kassem F, Viviani M, Agostini A, Faiz R, Pasi G (2019) LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. Pers Ubiquit Comput 23(2):181–197 · DOI
  18. Mittal M, Siriaraya P, Lee C, Kawai Y, Yoshikawa T, Shimojo S (2019) Accurate spatial mapping of social media with physical locations. IEEE BSD, Big Data, Los Angeles, USA, pp 9–12 Dec 2019. · DOI
  19. Mittal M, Iwendi C, Khanand S, Javed AR (2020) Analysis of security and energy efficiency for shortest route discovery in leach protocol using levenberg-marquardt neural network and gated recurrent unit for IDS. ETT, Wiley. · DOI
  20. Mizgajski J, Morzy M (2019) Affective recommender systems in the online news industry: how emotions influence reading choices. User Model User-Adap Inter 29(2):345–379 · DOI
  21. O'Mahony MP, Hurley NJ, & Silvestre GC (2005) Recommender systems: Attack types and strategies. In AAAI, pp. 334–339.
  22. Putri DCG, Leu JS, Seda P (2020) Design of an unsupervised machine learning-based movie recommender system. Symmetry 12(2):185 · DOI
  23. Shokeen J, Rana C (2020) A study on features of social recommender systems. Artif Intell Rev 53:965–988 · DOI
  24. Shriver D (2018) Assessing the quality and stability of recommender systems. University of Nebraska, Lincoln
  25. Si M, Li Q (2020) Shilling attacks against collaborative recommender systems: a review. Artif Intell Rev 53(1):291–319 · DOI
  26. Tong C, Yin X, Li J, Zhu T, Lv R, Sun L, Rodrigues JJ (2018) A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network. Comput J 61(7):949–958 · DOI
  27. Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550 · DOI
  28. Zhou Q, Wu J, Duan L (2020) Recommendation attack detection based on deep learning. J Inf Secur Appl 52:102493

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Metadata

Authors
Rani, S., Kaur, M., Kumar, M., Ravi, V., Ghosh, U., & Mohanty, J. R.
Affiliations
  • Chitkara University (IN)
  • Maharaja Ranjit Singh Punjab Technical University (IN)
  • Prince Mohammad bin Fahd University (SA)
  • Vanderbilt University (US)
  • KIIT University (IN)
Corresponding author
Rani, Shalli
ORCID
Venue
Soft Computing
Publisher
Springer Science and Business Media LLC
Published
2021-01-28
Volume
27
Issue
1
Pages
377-389
ISSN
  • 1432-7643 (ISSN-L)
  • 1433-7479
DOI
10.1007/s00500-021-05586-8
OpenAlex
W3124264680
Cited by
29
References
0
Citations by year
2026: 3 · 2025: 6 · 2024: 7 · 2023: 8 · 2022: 3 · 2021: 2
FWCI
4.52
Type
journal article
Language
en
Status
Retracted
Update notices
retraction (2024-10-28)
Open access
closed
Licence
Locations
Soft Computing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
shilling attacks, recommender systems, attack detection, filler particle size, Netflix dataset, boosted decision trees

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CoChat. (2026). Citation check: RETRACTED ARTICLE: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques. https://app.cochat.ai/papers/10.1007/s00500-021-05586-8
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Rani, S., Kaur, M., Kumar, M., Ravi, V., Ghosh, U., & Mohanty, J. R. (2023). RETRACTED ARTICLE: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques. Soft Computing. https://doi.org/10.1007/s00500-021-05586-8