SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

Saurabh Sharma¹, Ghanshyam Prasad Dubey², Harish Kumar Shakya³, Aditi Sharma^4*

^1,2Department of Computer Science and Engineering,

Amity School of Engineering and Technology (ASET),Amity University, Gwalior, India

³Department of Artificial Intelligence & Machine Learning, Manipal University Jaipur

⁴Department of Computer Sc. and Engg., Symbiosis Institute of Technology,

Symbiosis International (Deemed University), Pune, India

Emails: saurabhgyangit@gmail.com; gpdubey@gwa.amity.edu; harish.shakya@jaipur.manipal.edu; aditi.sharma@ieee.org

Abstract

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.

Keywords: Movie Recommendation; Enhanced SOM; Personalization; Hybrid Filtering; User Engagement