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ENHANCING MOVIE RECOMMENDATIONS USING CBCF-CNN WITH WORD CLOUD VISUALIZATIONS
The global video-on-demand market is projected to surpass $257 billion by 2027, with platforms like Netflix attributing over 80% of content watched to recommendation engines. This highlights the growing demand for intelligent, accurate, and personalized movie recommendation systems. However, existing systems face persistent issues such as data sparsity, cold-start problems, and lack of content diversity, which hinder their effectiveness and scalability. To address these limitations, this research introduces a novel hybrid recommendation framework named Content-Based Collaborative Filtering integrated with Convolutional Neural Networks (CBCF-CNN). The proposed model innovatively combines the strengths of content-based filtering, collaborative filtering, and deep learning to deliver a robust and scalable recommendation system. CBCF-CNN leverages user-item interaction matrices along with highdimensional feature extraction from movie metadata and visual content using CNNs. This deep integration allows the system to capture latent patterns and semantic similarities between items, thus mitigating the cold-start issue and enriching the recommendation space. Furthermore, the architecture enhances personalization by learning user-specific behavior from sparse data and enables real-time inference through parallelized CNN processing. Unlike traditional models that treat collaborative and content-based filtering separately, CBCF-CNN creates a unified representation that dynamically adapts to user preferences while promoting recommendation diversity and relevance. The model's ability to scale with massive datasets while maintaining low latency and high accuracy makes it suitable for deployment in large-scale platforms, offering a significant advancement over existing recommendation techniques
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