Hybrid Movie Recommendation System with Content-Based and Memory-Based Collaborative Filtering based on Deep Neural Network - Yang, Yongmao; Woradit, Kampol (ECTI Transactions on Electrical Engineering, Electronics, and Communications, 2025)
Recommendation systems assist users in filtering qualified data from vast datasets. Content-based and collaborative filtering techniques are the most widely used among the various recommendation models. In recent years, neural network algorithms have been employed to tackle recommendation problems. How- ever, while these methods reduce the error between true and predicted values, personalized recommenda- tions remain relatively lacking. To address this, we propose a movie recommendation system based on deep neural networks and user vocabulary preference features to alleviate cold start and sparsity issues, re- duce prediction errors, and improve recommendation efficiency. We evaluate our model using hit rate (HR) and average reciprocal hit rank (ARHR) as indica- tors, achieving an HR of 0.76 and an ARHR of 0.38. The robustness of our model is demonstrated through comparisons with other studies.