Comparative Analysis of Recommendation Algorithms: Collaborative, Content-Based and Hybrid Approaches

Recommendation systems have become a fundamental component of digital platforms, significantly improving user engagement and personalization. This study provides a comparative analysis of three major recommendation algorithms: collaborative filtering, content-based filtering, and hybrid models. The research aims to identify the most effective approach by evaluating each model's performance on the MovieLens dataset, which contains explicit user ratings and item metadata.The methodology involves preprocessing user-item interaction data, applying SVD++ for collaborative filtering, TF-IDF for content-based filtering, and integrating both in a hybrid approach. The evaluation metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, Recall, and F1-score, ensuring a comprehensive assessment of each model’s efficiency.Results indicate that the hybrid model outperforms standalone methods, achieving higher validation accuracy (0.8587) and lower RMSE (0.3233), demonstrating better generalization and recommendation quality. The collaborative filtering model, while effective (0.8549 validation accuracy), suffers from data sparsity, whereas content-based filtering exhibits limited diversity and overfitting tendencies (0.7409 validation accuracy).The study concludes that hybrid recommendation systems provide a more scalable and adaptive solution, balancing accuracy, novelty, and diversity. Future research may explore deep learning-based hybrid approaches to further optimize model performance and user engagement. These findings have practical implications for real-world recommender systems, particularly in domains requiring high personalization and content diversity