Hybrid Filtering Methods in Movie Recommendation: The Enhanced SOM Approach

Customized movie recommendations are becoming increasingly crucial in enhancing consumer satisfaction and involvement in an age where the majority of entertainment content is viewed digitally. This work employs advanced self-organizing maps (SOM) to provide a new approach for recommending movies. Unsupervised neural network models, commonly known as SOMs, are highly effective for recommendation systems due to their ability to effectively capture complex data structures. The proposed methodology entails collecting data on user-movie interactions, encompassing user ratings and the characteristics of the film. Before training an improved self-organizing map (SOM), the data is standardized to ensure uniformity. The Enhanced Self-Organizing Map's neighborhood function and variable learning rate allow it to precisely detect intricate patterns in the data. Subsequently, the trained SOM is utilized to identify similar users and flicks, enabling the provision of personalized movie suggestions. The system employs hybrid filtering methods to enhance the quality of recommendations. Content-based filtering utilizes movie attributes such as genres and descriptions, whereas collaborative filtering algorithms consider the interactions between users and things. These approaches generate well selected recommendations, leading to a comprehensive and diverse list of suggested films. The recommended solution is assessed based on user satisfaction and the precision of suggestions using pre-established criteria. The effectiveness of the Enhanced SOM-based movie recommendation approach has been confirmed by numerous research and real-world datasets. In order to improve the quality of recommendations, the system offers the ability to adjust parameters, grid sizes, and neighborhood functions. Considering all pertinent criteria, I recommend a movie that is strongly endorsed. Utilizing Enhanced SOM provides a new and dependable method for addressing the problem of personalized movie recommendations. This method provides precise recommendations for movies, as well as the ability to handle large amounts of data and adjust to different circumstances. Consequently, it becomes an essential tool for content platforms that aim to enhance user satisfaction. The system utilizes sophisticated self-organizing maps (SOMs) and hybrid filtering methods.