Enhancing Movie Category Prediction with Hybrid Models for Enhanced Interpretability
Seelam Namitha, J Prathibha, Thokala Karthik Reddy, Shanmugasundaram Hariharan, Vinay Kekreja, Andraju Bhanu Prasad · 2024
The paper presents an advanced method for predicting movie genres using a hybrid model that combines textual, visual, and numerical variables in a smooth manner. Our model achieves a remarkable accuracy of 84.73% by fusing feature-rich data, such as verbal descriptions, movie posters, and numerical metadata, with the strengths of deep learning approaches, such as recurrent neural networks (RNN) and long short-term memory (LSTM) networks. This extensive blending of modalities results in a genre classification scheme that is more complex and comprehensible. While the addition of textual data allows for a greater grasp of story the intricate details, the introduction of movie posters makes it easier to derive visual patterns. Furthermore, the numerical metadata offers insightful quantitative information. The model’s superiority in managing diverse and multimodal data is demonstrated through a number of experiments and comparisons with traditional algorithms, substantiating its performance. Along with to making significant contributions to the field of movie genre prediction, this study highlights the effectiveness of hybrid models in deriving insightful patterns from intricate, multimodal datasets.