Hybrid Filtering Techniques and Improved SOM for Next-Generation Film Recommendations
Keywords:
Movie Recommendation, Enhanced SOM, Personalization, Hybrid Filtering, User EngagementAbstract
Personalized movie recommendations are becoming increasingly important for increasing user satisfaction and engagement in an era where entertainment content is becoming more digitally connected. This work presents a novel approach to facilitate movie selection through the use of Enhanced Self-Organizing Maps (SOM). SOMs, or unsupervised neural network architectures, are helpful in recommendation systems because of their ability to recognize intricate patterns in data. The methodology described in this paper starts with gathering user-movie interaction data, such as user ratings and movie characteristics. This data is normalized before being used to train the model in order to preserve homogeneity. Subtle patterns in data can be effectively identified by the Enhanced SOM due to its adjustable learning rate and neighborhood function. The Enhanced SOM's capacity to identify related users and movies is used to generate personalized movie suggestions. The framework's incorporation of hybrid filtering approaches improves the caliber of recommendations. While content-based filtering uses movie attributes like genres and descriptions, collaborative filtering algorithms take advantage of user-item interactions. These methods yield recommendations that synergistically combine multiple filtering processes. The efficacy of the proposed resolution is meticulously evaluated by contrasting the precision of the recommendations and user contentment with pre-established standards. A thorough evaluation of real-world datasets supports the efficacy of the Enhanced SOM-based movie recommendation technique. To further enhance suggestion quality, the system offers options to modify grid sizes, neighborhood functions, and parameters. When taken as a whole, these features demonstrate how successful the suggested approach is in making personalized movie recommendations. Enhanced SOMs are a reliable model for content platforms that want to enhance user experiences by offering precise movie recommendations along with scalability and flexibility when paired with hybrid filtering techniques.
Downloads
References
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. [Google Scholar] [CrossRef]
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. [Google Scholar]
Gupta, S. A Literature Review on Recommendation Systems. Int. Res. J. Eng. Technol. 2020, 7, 3600–3605. [Google Scholar]
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. [Google Scholar]
Aggarwal, C.C. An Introduction to Recommender Systems. In Recommender Systems; Springer: Berlin/Heidelberg, Germany, 2016; pp. 1–28. [Google Scholar] [CrossRef]
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. [Google Scholar]
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. [Google Scholar] [CrossRef][Green Version]
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. [Google Scholar] [CrossRef]
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. [Google Scholar]
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. [Google Scholar] [CrossRef]
Çano, E.; Morisio, M. Hybrid recommender systems: A systematic literature review. Intell. Data Anal. 2017, 21, 1487–1524. [Google Scholar] [CrossRef][Green Version]
Schafer, J.B.; Konstan, J.A.; Riedl, J. E-commerce recommendation applications. Data Min. Knowl. Discov. 2001, 5, 115–153. [Google Scholar] [CrossRef]
Shen, J.; Zhou, T.; Chen, L. Collaborative filtering-based recommendation system for big data. Int. J. Comput. Sci. Eng. 2020, 21, 219–225. [Google Scholar] [CrossRef]
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. [Google Scholar] [CrossRef]
Katarya, R.; Verma, O.P. An effective collaborative movie recommender system with cuckoo search. Egypt. Inform. J. 2017, 18, 105–112. [Google Scholar] [CrossRef][Green Version]
Kumar, B.; Sharma, N. Approaches, Issues and Challenges in Recommender Systems: A Systematic Review. Indian J. Sci. Technol. 2016, 9, 1–12. [Google Scholar] [CrossRef]
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.
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.
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.
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.
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.
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.
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
