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Original Article
Hybrid Movie Recommendation System Using User Partitioning and Log-Likelihood Based Content Comparison
Dr. C. Lakshmi1
Abhishek2
Avinash N K3
Bhagavanthraya G4
Dhyana Chandra5
1 Professor, Department of Computer Science & Engineering Rajarajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 5 Department of Computer Science & Engineering Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 47-58
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250606008
References
1. N.Kannikaklang, S. Wongthanavasu, and W. Thamviset, ‘‘A hybrid recommender system for improving rating prediction of movie recommen dation,’’ in Proc. 19th Int. Joint Conf. Comput. Sci. Softw. Eng. (JCSSE), Jun. 2022, pp. 1–6.
2. P. G. Padti, K. Hegde, and P. Kumar, ‘‘Hybrid movie recommender sys tem,’’ Int. J. Res. Eng., Sci. Manage., vol. 4, no. 7, pp. 311–314, 2021.
3. P. K. Ismail, N. Musthafa, and C. G. Raji, ‘‘Hybrid recommender system using K-means clustering,’’ in Proc. 8th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), vol. 1, Mar. 2022, pp. 625–630.
4. M. S. Medikonduru, P. Bypureddy, V. P. Kancharla, V.N. H. Y. P. Kamarajugadda, and L. C. Attluri, ‘‘Structured analysis on movie recommendation system using machine learning,’’ in Proc. 6th Int. Conf. Intell. Comput. Control Syst. (ICICCS), May 2022, pp. 1297–1303.
5. Z. Strömqvist, ‘‘Matrix factorization in recommender systems: How sen sitive are matrix factorization models to sparsity?’’ Ph.D. thesis, Dept. Statistics, Uppsala Univ., Uppsala, Sweden, 2018.
6. S. Bag, S. K. Kumar, and M. K. Tiwari, ‘‘An efficient recommendation generation using relevant Jaccard similarity,’’ Inf. Sci., vol. 483, pp. 53–64, May 2019.
7. A.-Q. Tian, X.-Y. Wang, H. Xu, J.-S. Pan, V. Snášel, and H.-X. Lv, ‘‘Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improve ment,’’ Energy, vol. 294, May 2024, Art. no. 130927.
8. J.-S. Pan, A.-Q. Tian, V. Snášl, L. Kong, and S.-C. Chu, ‘‘Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon- inspired optimization with Taguchi method,’’ Energy, vol. 251, Jul. 2022, Art. no. 123863.
9. A.-Q. Tian, F.-F. Liu, and H.-X. Lv, ‘‘Snow geese algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems,’’ Appl. Math. Model., vol. 126, pp. 327–347, Feb. 2024.
10. M. MngomezuluandR.Ajoodha, ‘‘A content-based collaborative filtering movie recommendation system using keywords extractions,’’ in Proc. Int. Conf. Eng. Emerg. Technol. (ICEET), Oct. 2022, pp. 1–6.
11. Á. González, F. Ortega, D. Pérez-López, and S. Alonso, ‘‘Bias and unfair ness of collaborative filtering based recommender systems in MovieLens dataset,’’ IEEE Access, vol. 10, pp. 68429–68439, 2022.
12. M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. Rathore, ‘‘Movie recommender system using collaborative filtering,’’ in Proc. Int. Conf. Electron. Sustain. Commun. Syst. (ICESC), Jul. 2020, pp. 415–420.
2. P. G. Padti, K. Hegde, and P. Kumar, ‘‘Hybrid movie recommender sys tem,’’ Int. J. Res. Eng., Sci. Manage., vol. 4, no. 7, pp. 311–314, 2021.
3. P. K. Ismail, N. Musthafa, and C. G. Raji, ‘‘Hybrid recommender system using K-means clustering,’’ in Proc. 8th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), vol. 1, Mar. 2022, pp. 625–630.
4. M. S. Medikonduru, P. Bypureddy, V. P. Kancharla, V.N. H. Y. P. Kamarajugadda, and L. C. Attluri, ‘‘Structured analysis on movie recommendation system using machine learning,’’ in Proc. 6th Int. Conf. Intell. Comput. Control Syst. (ICICCS), May 2022, pp. 1297–1303.
5. Z. Strömqvist, ‘‘Matrix factorization in recommender systems: How sen sitive are matrix factorization models to sparsity?’’ Ph.D. thesis, Dept. Statistics, Uppsala Univ., Uppsala, Sweden, 2018.
6. S. Bag, S. K. Kumar, and M. K. Tiwari, ‘‘An efficient recommendation generation using relevant Jaccard similarity,’’ Inf. Sci., vol. 483, pp. 53–64, May 2019.
7. A.-Q. Tian, X.-Y. Wang, H. Xu, J.-S. Pan, V. Snášel, and H.-X. Lv, ‘‘Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improve ment,’’ Energy, vol. 294, May 2024, Art. no. 130927.
8. J.-S. Pan, A.-Q. Tian, V. Snášl, L. Kong, and S.-C. Chu, ‘‘Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon- inspired optimization with Taguchi method,’’ Energy, vol. 251, Jul. 2022, Art. no. 123863.
9. A.-Q. Tian, F.-F. Liu, and H.-X. Lv, ‘‘Snow geese algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems,’’ Appl. Math. Model., vol. 126, pp. 327–347, Feb. 2024.
10. M. MngomezuluandR.Ajoodha, ‘‘A content-based collaborative filtering movie recommendation system using keywords extractions,’’ in Proc. Int. Conf. Eng. Emerg. Technol. (ICEET), Oct. 2022, pp. 1–6.
11. Á. González, F. Ortega, D. Pérez-López, and S. Alonso, ‘‘Bias and unfair ness of collaborative filtering based recommender systems in MovieLens dataset,’’ IEEE Access, vol. 10, pp. 68429–68439, 2022.
12. M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. Rathore, ‘‘Movie recommender system using collaborative filtering,’’ in Proc. Int. Conf. Electron. Sustain. Commun. Syst. (ICESC), Jul. 2020, pp. 415–420.
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