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Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey

by Kanishkar Indira, Kiruthi Thaker
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 44
Year of Publication: 2024
Authors: Kanishkar Indira, Kiruthi Thaker
10.5120/ijca2023922852

Kanishkar Indira, Kiruthi Thaker . Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey. International Journal of Applied Information Systems. 12, 44 ( Jul 2024), 41-46. DOI=10.5120/ijca2023922852

@article{ 10.5120/ijca2023922852,
author = { Kanishkar Indira, Kiruthi Thaker },
title = { Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey },
journal = { International Journal of Applied Information Systems },
issue_date = { Jul 2024 },
volume = { 12 },
number = { 44 },
month = { Jul },
year = { 2024 },
issn = { 2249-0868 },
pages = { 41-46 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number44/data-security-concerns-in-approaches-to-overcome-cold-start-problem-in-recommender-systems-a-survey/ },
doi = { 10.5120/ijca2023922852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-20T21:32:36.010127+05:30
%A Kanishkar Indira
%A Kiruthi Thaker
%T Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 44
%P 41-46
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the subject of recommendation engines, the cold start problem is a significant research topic. Due to a lack of knowledge about the user and/or services, the recommendation system is unable to predict the user's preferences or interested products, resulting in a cold start. Many people have sought to overcome the cold start problem in recommending generic domains such as music, movies, E-Commerce, and travel websites using different types of machine learning models. This work provides a survey of the most recent to the traditional methods used for solving the cold start problem and also provides a holistic view of the adversarial attacks that are possible on the machine learning models used while trying to solve the cold start problem using the machine learning models.

References
  1. Wahab OA, Rjoub G, Bentahar J, Cohen R. Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems. Information Sciences. 2022 Jul 1;601:189-206.
  2. Annette J, Ruby, Aisha Banu W, Subash Chandran "Classification and Comparison of Cloud Renderfarm Services for Recommender Systems". Lecture Notes on Data Engineering and Communications Technologies, Springer, 2019.
  3. Ruby Annette et al. “A Cloud Service Providers Ranking System Using Ontology” International Journal of Scientific & Engineering Research 6 (4), 41-45, 2015.
  4. Wang H. DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems. arXiv preprint arXiv:2206.00151. 2022 May 31.
  5. Zhao X, Ren Y, Du Y, Zhang S, Wang N. Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder. arXiv preprint arXiv:2205.13795. 2022 May 27.
  6. Xu Y, Yang Y, Wang E. Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems. arXiv preprint arXiv:2204.12651. 2022 Apr 27.
  7. Shah AA, Venkateshwara H. Sparsity Regularization For Cold-Start Recommendation. arXiv preprint arXiv:2201.10711. 2022 Jan 26.
  8. Wang H. ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data. In2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE) 2021 Sep 24 (pp. 102-105). IEEE.
  9. Feng X, Chen C, Li D, Zhao M, Hao J, Wang J. CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 Oct 26 (pp. 484-493).
  10. Wang Z, Xiao W, Li Y, Chen Z, Jiang Z. LHRM: A LBS Based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform. In International Conference on Neural Information Processing 2020 Nov 18 (pp. 479-490). Springer, Cham.
  11. Wei Y, Wang X, Li Q, Nie L, Li Y, Li X, Chua TS. Contrastive learning for cold-start recommendation. In Proceedings of the 29th ACM International Conference on Multimedia 2021 Oct 17 (pp. 5382-5390).
  12. Briand L, Salha-Galvan G, Bendada W, Morlon M, Tran VA. A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 Aug 14 (pp. 2601-2609).
  13. Zhang Y, Maekawa T, Hara T. Using Social Media Background to Improve Cold-start Recommendation Deep Models. In2021 International Joint Conference on Neural Networks (IJCNN) 2021 Jul 18 (pp. 1-8). IEEE.
  14. Lai PL, Chen CY, Lo LW, Chen CC. ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks. arXiv preprint arXiv:2011.12566. 2020 Nov 25.
  15. Ruby Annette J, Aisha B W, Subash CP. A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services. International Journal of Electrical and Computer Engineering. 2018 Oct 1;8(5):3214.
  16. Annette J, Ruby, Aisha Banu W. “Ranking and Selection of Cloud Renderfarm Services”, Sadhana, 44:7, 2019.
  17. Zhou Y, Nadaf A. Embedded collaborative filtering for" cold start" prediction. arXiv preprint arXiv:1704.02552. 2017 Apr 9.
  18. Bernardi, Lucas Kamps, Jaap Kiseleva, Julia Mueller and Melanie, "The continuous cold start problem in e-commerce recommender systems”, CEUR Workshop Proceedings, Vol. 29, pp. 41-47, 2015.
  19. Ruby, Annette J., Banu W. Aisha, and Chandran P. Subash. "RenderSelect: a cloud broker framework for cloud renderfarm services." arXiv preprint arXiv:1611.10210 (2016).
  20. Ruby Annette J, and Aisha Banu. "A service broker model for cloud based render farm selection." arXiv preprint arXiv:1505.06542 (2015).
  21. Liu, Jin-Hu, Tao Zhou, Zi-Ke Zhang, Zimo Yang, Chuang Liu, and Wei-Min Li, "Promoting cold-start items in recommender systems”, PloS one, Vol. 9, 2014.
  22. Trevisiol, Michele, "Cold-start news recommendation with domain-dependent browse graph." Proceedings of the 8th ACM Conference on Recommender systems, ACM, 2014.
  23. Amatriain, Xavier. "Mining large streams of user data for personalized recommendations." ACM SIGKDD Explorations Newsletter , Vol 14, pp.37-48, 2013.
  24. Saveski, Martin, and Amin Mantrach, "Item cold-start recommendations: learning local collective embeddings." Proceedings of the 8th ACM Conference on Recommender systems, ACM, 2014.
  25. Yang, Xiwang, Yang Guo, and Yong Liu, "Bayesian-inference-based recommendation in online social networks." IEEE Transactions on Parallel and Distributed Systems, Vol 24, pp. 642-651, 2013.
  26. Chen, Chien Chin, Yu-Hao Wan, Meng-Chieh Chung, and Yu-Chun Sun, "An effective recommendation method for cold start new users using trust and distrust networks”, Information Sciences, Vol. 224, pp. 19-36, 2013.
  27. Krauss, Christopher, Lars George, and Stefan Arbanowski, "TV predictor: personalized program recommendations to be displayed on SmartTVs." Proceedings of the 2nd international workshop on big data, streams and heterogeneous source mining: Algorithms, systems, programming models and applications, ACM, 2013.
  28. Verbert, Katrien, Denis Parra, Peter Brusilovsky, and Erik Duval. "Visualizing recommendations to support exploration, transparency and controllability." In Proceedings of the 2013 international conference on Intelligent user interfaces, ACM, pp. 351-362, 2013.
  29. Gupta, Pankaj, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Zadeh. "Wtf: The who to follow service at twitter." In Proceedings of the 22nd international conference on World Wide Web, ACM, pp. 505-514, 2013.
  30. Bobadilla, Jesus, Fernando Ortega, Antonio Hernando, and Jesus Bernal. "A collaborative filtering approach to mitigate the new user cold start problem", Knowledge-Based Systems, Vol. 26, pp. 225-238, 2012.
  31. Kim, Heung-Nam, Abdulmotaleb El-Saddik, and Geun-Sik Jo, "Collaborative error-reflected models for cold-start recommender systems”, Decision Support Systems, Vol. 51,pp. 519-531, 2011.
  32. Park, Seung-Taek, and Wei Chu, "Pairwise preference regression for cold-start recommendation”, Proceedings of the third ACM conference on Recommender systems, ACM, pp. 21-28, 2009.
  33. S. Loh, F. Lorenzi, R. Granada, D. Lichtnow, LK. Wives and J.P. Oliveira, “Identifying similar users by their scientific publications to reduce cold start in recommender systems”, Proceedings of the 5th International Conference on Web Information Systems and Technologies (WEBIST2009),pp. 593-600, 2009.
  34. L. Martínez, L.G. Pérez and M.J. Barranco, “Incomplete preference relations to smooth out the cold-start in collaborative recommender systems”, Proceedings of the 28th North American Fuzzy Information Processing Society Annual Conference (NAFIPS2009), pp. 1-6, 2009.
  35. LT. Weng, Y. Xu, Y. Li and R. Nayak, “Exploiting item taxonomy for solving cold-start problem in recommendation making”, Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI2008), USA, pp. 113-120, 2008.
  36. Annette, J. Ruby, W. Aisha Banu, and P. Subash Chandran. "Rendering-as-a-Service: Taxonomy and Comparison". Procedia Computer Science 50 (2015): 276-281, Elsevier.
  37. Ruby Annette J, Aisha Banu W, and Shriram. "A Taxonomy and Survey of Scheduling Algorithms in Cloud: Based on task dependency." International Journal of Computer Applications, 82.15 (2013): 20-26.
  38. C.W. Leung, S.C. Chan and F.L Chung, “An empirical study of a cross-level association rule mining approach to cold-start recommendations”, Knowledge Based Systems, Vol. 21, pp. 515-529, 2008.
  39. S.T. Park, D.M. Pennock, O. Madani, N. Good and D. Coste, “Naive filterbots for robust cold-start recommendations”, Proceedings of Knowledge Discovery and Data Mining (KDD2006), pp. 699-705, 2006.
  40. P.B. Ryan, D. Bridge, “Collaborative recommending using formal concept analysis”, Knowledge Based Systems, Vol. 19, pp. 309-315, 2006.
  41. Schein, Andrew I., Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. "Methods and metrics for cold-start recommendations”, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp. 253-260, 2002.
  42. Ruby Annette J., Banu W. Aisha, and Chandran P. Subash. "Comparison of multi criteria decision making algorithms for ranking cloud renderfarm services." arXiv preprint arXiv:1611.10204 (2016).
  43. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In ICLR
  44. Yevgeniy Vorobeychik and Murat Kantarcioglu. 2018. Adversarial Machine Learning. Morgan & Claypool Publishers.
  45. Han Xiao, Huang Xiao, and Claudia Eckert. 2012. Adversarial Label Flips Attack on Support Vector Machines. In ECAI (Frontiers in Artificial Intelligence and Applications).
  46. Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, and Fabio Roli. 2018. Poisoning Behavioral Malware Clustering. arXiv (2018).
  47. Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012. Poisoning Attacks against Support Vector Machines. In ICML. icml.cc / Omnipress.
  48. Zach Jorgensen, Yan Zhou, and W. Meador Inge. 2008. A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters. J. Mach. Learn. Res. 9 (2008), 1115–1146
  49. Deldjoo Y, Noia TD, Merra FA. A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks. ACM Computing Surveys (CSUR). 2021 Mar 5;54(2):1-38.
  50. Annette R, Banu A. Sriram,“Cloud Broker for Reputation-Enhanced and QoS based IaaS Service Selection”. InProc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC, Elsevier 2014 (pp. 815-824).
  51. Ruby Annette J., Banu W. Aisha, and Chandran P. Subash. "Comparison of multi criteria decision making algorithms for ranking cloud renderfarm services." arXiv preprint arXiv:1611.10204 (2016).
  52. Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. In Proc. of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
  53. Konstantina Christakopoulou and Arindam Banerjee. 2019. Adversarial attacks on an oblivious recommender. In Proc. of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019. 322–330.
Index Terms

Computer Science
Information Sciences
Data Security
Recommender Systems
Cold Start Problem

Keywords

Cold start Recommendation Engine Recommender Systems Natural Language Processing NLP Adversarial attacks Security Machine Learning Cloud Computing Distributed Systems

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