Abstract. Recommender systems play a crucial role in personalized content delivery by leveraging user preferences and content attributes. This study evaluates three advanced recommendation models: Neural Collaborative Filtering (NCF), Graph Neural Network-based Content Model (GNN-based Content Model), and Hybrid Neural Network (HNN). Each model integrates deep learning techniques to enhance prediction accuracy and user experience. The NCF model employs a dual-branch structure consisting of Generalized Matrix Factorization (GMF) and a Multi-Layer Perceptron (MLP) to model non-linear user-item interactions. The GNN-based Content Model represents users and items as nodes in a bipartite graph, utilizing Graph Convolutional Networks (GCN) to propagate relational and content-based information across connections. Lastly, the Hybrid Neural Network combines collaborative filtering embeddings with content-based features, aligning content representation within the learned latent space. Our evaluation, based on the MovieLens dataset, demonstrates that the Hybrid Neural Network achieves the highest accuracy (85%), outperforming NCF (80%) and the GNN-based Content Model (77.5%). The hybrid approach benefits from both collaborative and content-driven features, leading to improved user-item match quality. The GNN-based Content Model, despite leveraging structured relationships, struggles with cold-start users due to reliance on content information.
These findings suggest that hybrid approaches are more effective in capturing diverse recommendation signals. Future work may focus on integrating transformer-based architectures and reinforcement learning to further enhance recommendation relevance and adaptability.

Keywords: Recommender systems, Deep learning, Collaborative filtering, Graph neural networks, Hybrid models, Personalization