2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 43-62, 2024 | Cite this article as | XML | Html | PDF
Saurabh Sharma 1 , Ghanshyam Prasad Dubey 2 , Harish Kumar Shakya 3 , Aditi Sharma 4 *
Doi: https://doi.org/10.54216/FPA.160204
In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.
Movie Recommendation , Enhanced SOM , Personalization , Hybrid Filtering , User Engagement
[1] Alyari, F.; Navimipour, N.J. Recommender systems: A systematic review of the state of the art literature and suggestions for future research. Kybernetes 2018, 47, 985.
[2] Caro-Martinez, M.; Jimenez-Diaz, G.; Recio-Garcia, J.A. A theoretical model of explanations in recommender systems. In Proceedings of the ICCBR, Stockholm, Sweden, 9–12 July 2018.
[3] Gupta, S. A Literature Review on Recommendation Systems. Int. Res. J. Eng. Technol. 2020, 7, 3600–3605.
[4] Abdulla, G.M.; Borar, S. Size recommendation system for fashion e-commerce. In Proceedings of the KDD Workshop on Machine Learning Meets Fashion, Halifax, NS, Canada, 14 August 2017.
[5] Aggarwal, C.C. An Introduction to Recommender Systems. In Recommender Systems; Springer: Berlin/Heidelberg, Germany, 2016; pp. 1–28.
[6] Ghazanfar, M.A.; Prugel-Bennett, A. A scalable, accurate hybrid recommender system. In Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 9–10 January 2010.
[7] Deldjoo, Y.; Elahi, M.; Cremonesi, P.; Garzotto, F.; Piazzolla, P.; Quadrana, M. Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 2016, 5, 99–113.
[8] Alamdari, P.M.; Navimipour, N.J.; Hosseinzadeh, M.; Safaei, A.A.; Darwesh, A. A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access 2020, 8, 115694–115716.
[9] Cami, B.R.; Hassanpour, H.; Mashayekhi, H. A content-based movie recommender system based on temporal user preferences. In Proceedings of the 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), Shahrood, Iran, 20–21 December 2017.
[10] Beniwal, R.; Debnath, K.; Jha, D.; Singh, M. Hybrid Recommender System Using Artificial Bee Colony Based on Graph Database. In Data Analytics and Management; Springer: Berlin/Heidelberg, Germany, 2021; pp. 687–699.
[11] Çano, E.; Morisio, M. Hybrid recommender systems: A systematic literature review. Intell. Data Anal. 2017, 21, 1487–1524.
[12] Schafer, J.B.; Konstan, J.A.; Riedl, J. E-commerce recommendation applications. Data Min. Knowl. Discov. 2001, 5, 115–153.
[13] Shen, J.; Zhou, T.; Chen, L. Collaborative filtering-based recommendation system for big data. Int. J. Comput. Sci. Eng. 2020, 21, 219–225.
[14] Dakhel, G.M.; Mahdavi, M. A new collaborative filtering algorithm using K-means clustering and neighbors’ voting. In Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS), Malacca, Malaysia, 5–8 December 2011; pp. 179–184
[15] Katarya, R.; Verma, O.P. An effective collaborative movie recommender system with cuckoo search. Egypt. Inform. J. 2017, 18, 105–112.
[16] Kumar, B.; Sharma, N. Approaches, Issues and Challenges in Recommender Systems: A Systematic Review. Indian J. Sci. Technol. 2016, 9, 1–12
[17] Phani, S.. , Sandeep, Kotte. , Raghavendra, N.. , Sharma, Aditi. , Pandey, Jitendra. , Chouhan, Vikas. Outlier Management and its Impact on Diabetes Prediction: A Voting Ensemble Study. Journal of Journal of Intelligent Systems and Internet of Things, vol. 12, no. 1, 2024, pp. 08-19. DOI: https://doi.org/10.54216/JISIoT.120101
[18] Sharma S, Shakya HK. Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems. InInternational Conference on Computing, Communications, and Cyber-Security 2022 Oct 21 (pp. 371-388). Singapore: Springer Nature Singapore.
[19] Sharma S, Shakya HK. Recommendation System for Movies Using Improved version of SOM with Hybrid Filtering Methods. In2023 6th International Conference on Information Systems and Computer Networks (ISCON) 2023 Mar 3 (pp. 1-7). IEEE.
[20] S., Malathi. , S., Hemamalini. , M., Ashwin. , Benny, Rijo. Knowledge Navigator: Revolutionizing Education through LLMs in Generative AI. Journal of Fusion: Practice and Applications, vol. 16, no. 1, 2024, pp. 209-222. DOI: https://doi.org/10.54216/FPA.160114
[21] Sharma S, Shakya HK. Recommendation Systems for a Group of Users Which Recommend Recent Attention: Using Hybrid Recommendation Model. InInternational Conference on Advanced Communication and Intelligent Systems 2022 Oct 21 (pp. 659-672). Cham: Springer Nature Switzerland.
[22] Mohamed, A. H., E. Omar, A. (2024). Improving Arabic Spam classification in social media using hyperparameters tuning and Particle Swarm Optimization. Journal of Fusion: Practice and Applications, 16( 1), 08-22. DOI: https://doi.org/10.54216/FPA.160101
[23] Shanker, Jata. , K.Gupta, N.. , Sharma, Aditi. Enhanced Heart Disease Prediction Using Machine Learning Techniques. Journal of Journal of Intelligent Systems and Internet of Things, vol. 12, no. 2, 2024, pp. 19-33. DOI: https://doi.org/10.54216/JISIoT.120202
[24] Sharma, S., Shakya, H.K. and Marriboyina, V., 2021. A location based novel recommender framework of user interest through data categorization. Materials Today: Proceedings, 47, pp.7155-7161.
[25] Dash, R.K., Nguyen, T.N., Cengiz, K. et al. Fine-tuned support vector regression model for stock predictions. Neural Comput & Applic 35, 23295–23309 (2023). https://doi.org/10.1007/s00521-021-05842-w
[26] S. Phani Praveen, Kotte Sandeep, N. Raghavendra Sai, Aditi Sharma, Jitendra Pandey, Vikas Chouhan. (2024). Outlier Management and its Impact on Diabetes Prediction: A Voting Ensemble Study. Journal of Journal of Intelligent Systems and Internet of Things, 12 ( 1 ), 08-19 (Doi : https://doi.org/10.54216/JISIoT.120101)
[27] Sharma, S., Shakya, H.K. and Mishra, A., 2022. 11 Study and Estimation of Existing Software Quality. Multi-Criteria Decision Models in Software Reliability: Methods and Applications, p.235.
[28] Sharma S, Mishra A, Shakya HK. A Time-Variant Software Stability Model for Error Detection. InMulti-Criteria Decision Models in Software Reliability 2022 Nov 30 (pp. 277-290). CRC Press.
[29] Sharma S, Shakya HK. Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems. In International Conference on Computing, Communications, and Cyber-Security 2022 Oct 21 (pp. 371-388). Singapore: Springer Nature Singapore.
