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Original Article
Movie Recommendation System: A combined approach
Sachin Chawhan1
Dr. Minakshmi Shaw2
Dr. M. Sheshikala3
Dr. Balajee Maram4
1 Assistant professor, Department of Computer Science and engineering, Siddhartha Institute of Technology & Sciences, Hyderabad, Telangana, India. 2 Assistant professor, School of CS &AI, SR University, Warangal, Telangana, India. 3 Professor and Head, computer science and engineering, SR University, Warangal, Telangana, India 4 Professor, School of CS &AI, SR University, Warangal, Telangana, India
Published Online: November-December 2025
Pages: 39-42
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506006
References
1. Zhang, Jiang, et al. "Personalized real-time movie recommendation system: Practical prototype and evaluation."
2. Das, Debashis, Laxman Sahoo, and Sujoy Datta. "A survey on recommendation system." International Journal of Computer Applications160.7 (2017).
3. Ahmed, Muyeed, Mir Tahsin Imtiaz, and Raiyan Khan. "Movie recommendation system using clustering and pattern recognition network." 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2018.
4. Arora, Gaurav, et al. "Movie recommendation system based on users’ similarity." International Journal of Computer Science and Mobile Computing 3.4 (2014): 765-770.
5. Subramaniyaswamy, V., et al. "A personalised movie recommendation system based on collaborative filtering." International Journal of High Performance Computing and Networking 10.1-2 (2017): 54-63.
6. Rajarajeswari, S., et al. "Movie Recommendation System." Emerging Research in Computing, Information, Communication and Applications. Springer, Singapore, 2019. 329-340.
7. X. Y. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques. Adv. Artif. Intell., vol. 2009, p. 4, 2009.
8. Y. Shi, M. Larson, and A. Hanjalic, Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges, ACM Comput. Surv., vol. 47, no. 1, pp. 3, 2014.
9. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Itembased collaborative filtering recommendation algorithms, in Proc. 10th Int. Conf. World Wide Web, Hong Kong, China, 2001.
10. [10] M. Deshpande and G. Karypis, Item-base d top-N recommendation algorithms, ACM Trans. Inf. Syst., vol.22, no. 1, pp. 143–177, 2004.
11. Jalali M, Mustapha N, Sulaiman M, Mamay A. WebPUM: A Web-based recommendation system to predict user future movements.Exp Syst Applicat, March 2010
12. Herlocker, J. A. Konstan, and J. Riedl, An empirical analysis of design choices in neighborhoodbased collaborative filtering algorithms, Information Retrieval, vol. 5, no. 4, pp. 287–310, 2002.
13. G. Adomavicius and A. Tuzhilin, “Context-aware Recommender Systems,” in Recommender Systems Handbook: A Complete Guide for Research Scientists And Practitioners, Springer, 2010.
14. T. Bogers, “Movie recommendation using random Walks over the contextual graph,” in Proc. of the 2nd Workshop on Context-Aware Recommender Systems, 2010.
15. Tang, T. Y., & McCalla, “A multi-dimensional paper recommender: Experiments and evaluations,” IEEE Internet Computing, 13(4),34–41, 2009.
16. Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, “Item- basedcollaborative filtering recommendation algorithms,”In: Proceedings of the 10th international World Wide Web conference , pp. 285–295.
17. W. Woerndl and J. Schlichter, “Introducing Context into Recommender Systems,”Muenchen,Germany: Technische Universitaet Muenchen, pp. 138- 140.
18. P. Li, and S. Yamada, “A Movie Recommender System Based on Inductive Learning,” IEEE Conf. on Cybernetics and Int elligent System, pp.318-323, 2004.
2. Das, Debashis, Laxman Sahoo, and Sujoy Datta. "A survey on recommendation system." International Journal of Computer Applications160.7 (2017).
3. Ahmed, Muyeed, Mir Tahsin Imtiaz, and Raiyan Khan. "Movie recommendation system using clustering and pattern recognition network." 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2018.
4. Arora, Gaurav, et al. "Movie recommendation system based on users’ similarity." International Journal of Computer Science and Mobile Computing 3.4 (2014): 765-770.
5. Subramaniyaswamy, V., et al. "A personalised movie recommendation system based on collaborative filtering." International Journal of High Performance Computing and Networking 10.1-2 (2017): 54-63.
6. Rajarajeswari, S., et al. "Movie Recommendation System." Emerging Research in Computing, Information, Communication and Applications. Springer, Singapore, 2019. 329-340.
7. X. Y. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques. Adv. Artif. Intell., vol. 2009, p. 4, 2009.
8. Y. Shi, M. Larson, and A. Hanjalic, Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges, ACM Comput. Surv., vol. 47, no. 1, pp. 3, 2014.
9. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Itembased collaborative filtering recommendation algorithms, in Proc. 10th Int. Conf. World Wide Web, Hong Kong, China, 2001.
10. [10] M. Deshpande and G. Karypis, Item-base d top-N recommendation algorithms, ACM Trans. Inf. Syst., vol.22, no. 1, pp. 143–177, 2004.
11. Jalali M, Mustapha N, Sulaiman M, Mamay A. WebPUM: A Web-based recommendation system to predict user future movements.Exp Syst Applicat, March 2010
12. Herlocker, J. A. Konstan, and J. Riedl, An empirical analysis of design choices in neighborhoodbased collaborative filtering algorithms, Information Retrieval, vol. 5, no. 4, pp. 287–310, 2002.
13. G. Adomavicius and A. Tuzhilin, “Context-aware Recommender Systems,” in Recommender Systems Handbook: A Complete Guide for Research Scientists And Practitioners, Springer, 2010.
14. T. Bogers, “Movie recommendation using random Walks over the contextual graph,” in Proc. of the 2nd Workshop on Context-Aware Recommender Systems, 2010.
15. Tang, T. Y., & McCalla, “A multi-dimensional paper recommender: Experiments and evaluations,” IEEE Internet Computing, 13(4),34–41, 2009.
16. Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, “Item- basedcollaborative filtering recommendation algorithms,”In: Proceedings of the 10th international World Wide Web conference , pp. 285–295.
17. W. Woerndl and J. Schlichter, “Introducing Context into Recommender Systems,”Muenchen,Germany: Technische Universitaet Muenchen, pp. 138- 140.
18. P. Li, and S. Yamada, “A Movie Recommender System Based on Inductive Learning,” IEEE Conf. on Cybernetics and Int elligent System, pp.318-323, 2004.
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