Hybrid Filtering Techniques and Improved SOM for Next-Generation Film Recommendations

Abstract 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.