Major Challenges of Recommender System and Related Solutions

Authors

  • Surya Naga Sai Lalitha Chirravuri M.Tech Scholar, Department of Computer Science & Engineering, Vishnu Institute of Technology, Bhimavaram, India Author
  • Kali Pradeep Immidi Assistant Professor, Department of Computer Science & Engineering, Vishnu Institute of Technology, Bhimavaram, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Recommender system, content-based filtering, collaborative filtering, Deep Learning

Abstract

 Recommender system is a very young area  of machine learning & Deep Learning research. The basic  goal of the recommender system is to create a relationship  between items and consumers. The relationship provides  recommendations based on user interest. content-based,  collaborative, demographic, hybrid filtering, knowledge based, utility-based, classification model are well-known  recommender models. The model uses an item's  specifications in content-based filtering to suggest other  objects with similar features. Collaborative filtering takes  into the user's previous activity which means the user has  previously viewed or purchased, as well as ratings  Provided by the user to those items and similar conclusions  reached by other users' item lists. View user profile data  such as age category, gender, education, and living area to  detect commonalities with other profiles.[31] All three  filtering techniques are used in hybrid filtering. In the  process of recommendations, various challenges are faced  by the system. So, this paper lists various solutions by  researchers in recent days. 

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References

Motadoo, Sahil, "Resolving Cold Start Problem Using User Demographics and Machine Learning Techniques for Movie Recommender Systems" (2018). Master's Projects. 649.

Youssouf ELALLIOUI,” A novel approach to solve the new user cold-start problem in recommender systems using collaborative filtering” International Journal of Scientific & Engineering Research Volume 8, Issue 11, PP. November-2017.

Bakhshandegan Moghaddam, Farshad,” Cold Start Solutions For Recommendation Systems”, Elahi, Mehdi, PP. 2019/05/01, DOI: 10.13140/RG.2.2.27407.02725. [4] S., Sangeetha, Gangadharan, Sudha,” Privacy preserving hybrid recommender system based on deep learning”, Turkish Journal of Electrical Engineering and Computer Sciences, volume 29, PP: 2021/09/23, DOI: 10.3906/elk-2010-40

Shahriar Badsha1 • Xun Yi1 • Ibrahim Khalil,” A Practical Privacy-Preserving Recommender System” Data Sci. Eng,PP:2016, 1(3):161–177,DOI 10.1007/s41019- 016-0020-2.

Sahoo, Abhaya, Pradhan, Chittaranjan,” Accuracy‐ Assured Privacy‐Preserving Recommender System Using Hybrid‐Based Deep Learning Method” Recommender System with Machine Learning and Artificial Intelligence (pp.101-120), PY: 2020/06/17.

S. Badsha, X. Yi, I. Khalil, and E. Bertino, "Privacy Preserving User-Based Recommender System," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, pp. 1074-1083, DOI: 10.1109/ICDCS.2017.248.

Ghazanfar, Mustansar Ali, Prugel-Bennett, "Fulfilling the Needs of Gray-Sheep Users in Recommender Systems, A Clustering Solution" PY: 2011/01/21.

HyeungillLee, JungwooLee,” Scalable deep learning based recommendation systems”, Seoul National University, Seoul, Republic of Korea, volume 8, issue 2, PY: June 2019, https://doi.org/10.1016/j.icte.2018.05.003. [10] Gabor Takacs, Istvan Pilaszy, Bottyan Nemeth, Domonkos Tikk,” Scalable Collaborative Filtering Approaches for Large Recommender Systems”, Journal of

Machine Learning Research 10 (2009) 623-656. [11] Abhishek Srivastava, Pradip Kumar, BalaBipul Kumar,” Transfer Learning for Resolving Sparsity Problem in Recommender Systems: Human Values Approach”, JISTEM J.Inf.Syst. Technol. Manag. 14 (3) • Dec 2017, https://doi.org/10.4301/S1807- 17752017000300002.

Nadia F. Al-Bakril, Soukaena Hassan Hashim,” Reducing Data Sparsity in Recommender Systems”,

Journal of Al-Nahrain University, Vol.21 (2), June 2018, pp.138-147.

Gopal Behera, Neeta Nain,” Handling data sparsity via item metadata embedding into deep collaborative recommender system” PY:27December2021, https://doi.org/10.1016/j.jksuci.202

12.021

Mahsa Ebrahimian and Rasha Kashef,” Detecting Shilling Attacks Using Hybrid Deep Learning Models”, Electrical, Computer, and Biomedical Engineering Department, py: 31 October 2020.

S. Alonso, J. Bobadilla, F. Ortega, and R. Moya, "Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems," in IEEE Access, vol. 7, pp. 41782-41798, 2019, DOI: 10.1109/ACCESS.2019.2905862.

Takeuchi, Shahpar,” Learning Complex Users' Preferences for Recommender Systems”, py: 2021/07/03. [17] Negar Hariri, Bamshad Mobasher, Robin Burke,” Adapting to User Preference Changes in Interactive Recommendation”, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015).

Kostova, Patty, Jawaheer, Ganesh, Weller, Peter,” Modeling User Preferences in Recommender Systems”, ACM Transactions on Interactive Intelligent Systems,

volume4, py: 2014/06/01, DOI: 10.1145/2512208. [19] Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, and Alexander Tuzhilin. 2020. PURS: Personalized Unexpected Recommender System for Improving User Satisfaction. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20), September 22–26, 2020, Virtual Event, Brazil. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3383313.3412238. [20] Said, A., Bellogín, “A. Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems”, User Model User-Adap Inter 28, 97–125 (2018). https://doi.org/10.1007/s11257- 018-9202-0.

M. Hassan and M. Hamada, "Improving prediction accuracy of multi-criteria recommender systems using adaptive genetic algorithms," 2017 Intelligent Systems Conference (IntelliSys), 2017, pp. 326-330, DOI: 10.1109/IntelliSys.2017.8324313

Shahbazi, Zeinab, Byun, Yungcheol,” Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches”, Symmetry, volume 12, py: 2020/09/22, DOI: 10.3390/sym12091566.

Yadav, Naina, Kumar, Rajesh, Singh, Anil, Pal, Sukomal,” Diversity in Recommendation System: A Cluster-Based Approach”, py: 2021/01/01, DOI: 10.1007/978-3-030-49336-3_12.

W. Yang, S. Fan, and H. Wang, "An Item-Diversity Based Collaborative Filtering Algorithm to Improve the Accuracy of Recommender System," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet

(SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018, pp. 106-110, DOI: 10.1109/SmartWorld.2018.00053.

J. Yuan, W. Shalaby, M. Korayem, D. Lin, K. AlJadda and J. Luo, "Solving the cold-start problem in large-scale recommendation engines: A deep learning approach," 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 1901-1910, DOI: 10.1109/BigData.2016.7840810.

R. Kataria and O. P. Verma, "Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging," 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), 2017, pp. 52-57, DOI: 10.1109/CSCloud.2017.24.

M. A. Ghazanfar and A. Prugel-Bennett, "A Scalable, Accurate Hybrid Recommender System," 2010 Third International Conference on Knowledge Discovery and Data Mining, 2010, pp. 94-98, DOI: 10.1109/WKDD.2010.117.

J. F. G. da Silva, N. N. de Moura Junior, and L. P. Caloba, "Effects of Data Sparsity on Recommender Systems based on Collaborative Filtering," 2018 International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1-8, DOI: 10.1109/IJCNN.2018.8489095.

W. Bhebe and O. P. Kogeda, "Shilling attack detection in Collaborative Recommender Systems using a Meta Learning strategy," 2015 International Conference on Emerging Trends in Networks and Computer Communications (NCC), 2015, pp. 56-61, DOI: 10.1109/ETNCC.2015.7184808.

K. Inuzuka, T. Hayashi and T. Takagi "Recommendation System Based on Prediction of User Preference Changes," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2016, pp. 192-199, DOI: 10.1109/WI.2016.0036.

Negar Hariri, Bamshad Mobasher, Robin Burke,” Adapting to User Preference Changes in Interactive Recommendation”, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015).

Shubham Kumar Agrawal, Recommendation System - Understanding The Basic Concepts, July 13,2021.[online],Available :

https://www.analyticsvidhya.com/blog/2021/07/recomme ndation-system-understanding-the-basic-concepts. [Accessed: 10- feb- 2022].

Baptiste Rocca,” Introduction to recommender systems”, Jun 3, 2019.Available: https://towardsdatascience.com/introduction-to recommender-systems-6c66cf15ada, [Accessed: 10- feb 2022].

Carlos pinela, Recommender Systems — User-Based and Item-Based Collaborative Filtering, Nov 6, 2017,[online].Available:

https://medium.com/@cfpinela/recommender-systems

user-based-and-item-based-collaborative-filtering 5d5f375a127f. [Accessed: 12- feb- 2022].

Denise chen,” Recommender System — Matrix Factorization”, Jul 8, 2020,[online].Available: https://towardsdatascience.com/recommendation-system matrix-factorization-d61978660b4b,[Accessed: 12- feb 2022].

Dr.vaibhav kumar,”Singular value decomposition and it’s applications”,mar 28 2020,[online],Available: https://analyticsindiamag.com/singular-value decomposition-svd-application-recommender system/,[Accessed:15-feb-2022].

“Classifying Different Types of Recommender Systems”, 14 November 2015,[online],Available: https://www.bluepiit.com/blog/classifying-recommender systems/#:~:text=There%20are%20majorly%20six%20ty pes,system%20and%20Hybrid%20recommender%20syst em. [Accessed: 15- feb- 2022].

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Published

2022-03-30

Issue

Vol. 10 No. 2 (2022): International Journal of Innovative Research in Computer Science and Technology (Mar ) 2022

Section

Articles

How to Cite

Major Challenges of Recommender System and Related Solutions . (2022). International Journal of Innovative Research in Computer Science & Technology, 10(2), 10–18. https://doi.org/10.55524/
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