A Hybrid Movie Recommendation System to Address Data Sparsity Using Genre-Based K-Means and Neural Collaborative Filtering

Recommendation systems play a crucial role in helping users navigate the overwhelming volume of information on digital platforms. However, conventional Collaborative Filtering (CF) methods often suffer from data sparsity, leading to reduced prediction accuracy and limited recommendation diversity. To address this challenge, this study proposes a hybrid recommendation model that integrates K-Means clustering based on genre, release year, and rating statistics into the Neural Collaborative Filtering (NCF) framework. Unlike previous works that rely on a single dimension like genre or demographics for clustering, our model uniquely combines multiple content-based features. Furthermore, we explicitly integrate the cluster labels as additional embedding features within the NCF framework, enabling more nuanced and context-aware representation learning. Using the MovieLens Latest-Small dataset, our hybrid model significantly outperforms the baseline NCF across all metrics, achieving a Mean Absolute Error (MAE) of 0.6097, a Root Mean Square Error (RMSE) of 0.7946, and improvements in Precision@10 (0.6065) and Recall@10 (0.7063). These findings highlight the effectiveness of our novel, content-aware clustering approach in deep learning recommenders, resulting in more accurate, diverse, and contextually relevant movie suggestions.