ARCHIVES
Original Article
Digital Platform Recommendation System with Scalable model using Clustering
Dakshayani R N R1
Tejal N R2
1 Department of Computer Science and Engineering, Anna University, Madurai, Tamilnadu, India. 2 Department of Remote Sensing and GIS, Bharathidasan University, Trichy, Tamilnadu, India.
Published Online: September-December 2025
Pages: 160-166
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403026
References
1. D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol. 9, Art. no. 59, 2022. DOI: 10.1186/s40537-022-00592-5.
2. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,”IEEE Trans. Knowledge Data Eng., vol. 17, no. 6, pp. 734–749, 2005. DOI: 10.1109/TKDE.2005.99.
3. A. K. Jain, “Data clustering: A review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999. DOI: 10.1145/331499.331504.
4. W. McKinney, “Data Structures for Statistical Computing in Python,” in Proc. 9th Python in Science Conf., 2010, pp. 51–56. DOI: 10.25080/Majora-92bf1922-00a.
5. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Machine Learning Research, vol. 12, pp. 2825–2830, 2011. DOI: 10.48550/arXiv.1201.0490.
6. J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. DOI: 10.1109/MCSE.2007.55.
7. M. L. Waskom, “seaborn: Statistical data visualization,” J. Open Source Software, vol. 6, no. 60, p. 3021, 2021. DOI: 10.21105/joss.03021.
8. SimilarWeb, “Website Traffic & Analytics,” Oct. 2025. [Online]. Available: https://www.similarweb.com
9. Statista, “Global Web Usage Statistics and Popular Websites,” Oct. 2025. [Online]. Available: https://www.statista.com
10. Qualys SSL Labs, “SSL Server Test,” Oct. 2025. [Online]. Available: https://www.ssllabs.com/ssltest/
11. Mozilla Observatory, “Web Security & Privacy Scanner,” Oct. 2025. [Online]. Available: https://observatory.mozilla.org
12. Google, “PageSpeed Insights,” Web Performance Metrics & Accessibility Scores. Oct. 2025. [Online]. Available: https://pagespeed.web.dev
13. WebAIM, “Contrast Checker for Accessibility Compliance,” Oct. 2025. [Online]. Available: https://webaim.org/resources/contrastchecker/
14. G2 Crowd, “Software Reviews for Productivity and Collaboration Tools,” Oct. 2025. [Online]. Available: https://www.g2.com
15. Capterra, “User Reviews and Product Features for Business Software,” Oct. 2025. [Online]. Available: https://www.capterra.com
16. M. F. Porter, R. A. Baeza-Yates, and B. Ribeiro-Neto, “Measuring Web Popularity and Engagement,” ACM Transactions on the Web, vol. 15, no. 2, pp. 1–25, Apr. 2021.DOI: 10.1145/3439863
17. Google, “Core Web Vitals: Essential metrics for a healthy site,” Google Developers Documentation, 2024. [Online]. Available: https://developers.google.com/search/docs/appearance/core-web-vitals
18. J. Nielsen, Usability Engineering. Cambridge, MA: Academic Press, 1993.DOI: 10.1016/C2009-0-21559-6
19. T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. ACM SIGKDD, 2002, pp. 133–142. DOI: 10.1145/775047.775067
20. D. Wang, Y. Wang, and Y. Zhang, “Explainable Recommendation: A Survey and New Perspectives,” Found. Trends Inf. Retr., vol. 14, no. 1, pp. 1–101, 2020. DOI: 10.1561/1500000066
2. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,”IEEE Trans. Knowledge Data Eng., vol. 17, no. 6, pp. 734–749, 2005. DOI: 10.1109/TKDE.2005.99.
3. A. K. Jain, “Data clustering: A review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999. DOI: 10.1145/331499.331504.
4. W. McKinney, “Data Structures for Statistical Computing in Python,” in Proc. 9th Python in Science Conf., 2010, pp. 51–56. DOI: 10.25080/Majora-92bf1922-00a.
5. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Machine Learning Research, vol. 12, pp. 2825–2830, 2011. DOI: 10.48550/arXiv.1201.0490.
6. J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. DOI: 10.1109/MCSE.2007.55.
7. M. L. Waskom, “seaborn: Statistical data visualization,” J. Open Source Software, vol. 6, no. 60, p. 3021, 2021. DOI: 10.21105/joss.03021.
8. SimilarWeb, “Website Traffic & Analytics,” Oct. 2025. [Online]. Available: https://www.similarweb.com
9. Statista, “Global Web Usage Statistics and Popular Websites,” Oct. 2025. [Online]. Available: https://www.statista.com
10. Qualys SSL Labs, “SSL Server Test,” Oct. 2025. [Online]. Available: https://www.ssllabs.com/ssltest/
11. Mozilla Observatory, “Web Security & Privacy Scanner,” Oct. 2025. [Online]. Available: https://observatory.mozilla.org
12. Google, “PageSpeed Insights,” Web Performance Metrics & Accessibility Scores. Oct. 2025. [Online]. Available: https://pagespeed.web.dev
13. WebAIM, “Contrast Checker for Accessibility Compliance,” Oct. 2025. [Online]. Available: https://webaim.org/resources/contrastchecker/
14. G2 Crowd, “Software Reviews for Productivity and Collaboration Tools,” Oct. 2025. [Online]. Available: https://www.g2.com
15. Capterra, “User Reviews and Product Features for Business Software,” Oct. 2025. [Online]. Available: https://www.capterra.com
16. M. F. Porter, R. A. Baeza-Yates, and B. Ribeiro-Neto, “Measuring Web Popularity and Engagement,” ACM Transactions on the Web, vol. 15, no. 2, pp. 1–25, Apr. 2021.DOI: 10.1145/3439863
17. Google, “Core Web Vitals: Essential metrics for a healthy site,” Google Developers Documentation, 2024. [Online]. Available: https://developers.google.com/search/docs/appearance/core-web-vitals
18. J. Nielsen, Usability Engineering. Cambridge, MA: Academic Press, 1993.DOI: 10.1016/C2009-0-21559-6
19. T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. ACM SIGKDD, 2002, pp. 133–142. DOI: 10.1145/775047.775067
20. D. Wang, Y. Wang, and Y. Zhang, “Explainable Recommendation: A Survey and New Perspectives,” Found. Trends Inf. Retr., vol. 14, no. 1, pp. 1–101, 2020. DOI: 10.1561/1500000066
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