Peningkatan Akurasi Rekomendasi Film Menggunakan Neural Collaborative Filtering dengan Arsitektur RecommenderNet - Sukmana, Dimas; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki (Institut Teknologi Garut, 2025)
The rapid growth of the film industry and streaming platform users has given rise to the challenge of information overload, where users find it difficult to find films that suit their preferences amid the abundance of content choices. This study aims to develop a Neural Collaborative Filtering (NCF)-based movie recommendation system model with a RecommenderNet architecture to improve prediction accuracy and personal recommendation relevance. The model was evaluated using the Root Mean Square Error (RMSE) metric to assess rating prediction accuracy and Normalized Discounted Cumulative Gain (NDCG@100) to measure recommendation quality and order. The results show that the model achieves an RMSE of 0.1946 and an NDCG@100 of 0.8136, indicating the model's ability to learn user preferences and generate relevant and well-ordered recommendations. This research contributes to the development of more effective and personalized recommendation systems in the digital streaming domain and offers an efficient approach to reducing the impact of information overload and improving the user experience.