Advanced Deep Learning Models for Improving Movie Rating Predictions: A Benchmarking Study

• Deep learning models benchmarked for accurate movie rating prediction. • Novel integration of sentiment analysis and advanced feature extraction techniques. • Comparative performance evaluation identifies the most effective predictive model. • Hyperparameter tuning optimizes models for improved recommendation accuracy. • Comprehensive analysis enhances movie rating prediction using real-world datasets. Predicting movie ratings very precisely has become a vital aspect of personalized recommendation systems, which requires robust and high-performing models. for evaluating the effectiveness in predicting movie ratings, this study conducts a comprehensive performance analysis of various deep learning architectures, which includes BiLSTM, CNN + LSTM, CNN + GRU, CNN + Attention, CNN, VAE, Simple RNN, GRU + Attention, Transformer Encoder, FNN and ResNet. Here each model’s performance is evaluated on movie reviews’ dataset, enhanced with sentiment scores and user ratings, by using a range of evaluation metrics: Mean Absolute Error (MAE), R² score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Explained Variance. Here the results highlight distinct strengths and weaknesses among the models, in which VAE model consistently delivering superior accuracy, whereas attention-based models prove prominent improvements in interpretability and generalization. This analysis offers important insights into choosing models for movie recommendation systems, which also highlights the balance between prediction accuracy and computational efficiency. The discoveries from this study serve as a benchmark for future developments in movie rating prediction, supporting the researchers and practitioners in augmenting recommendation system performance.