Dual module- wider and deeper stochastic gradient descent and dropout based dense neural network for movie recommendation

In streaming services such as e-commerce, suggesting an item plays an important key factor in recommending the items. In streaming service of movie channels like Netflix, amazon recommendation of movies helps users to find the best new movies to view. Based on the user-generated data, the Recommender System(RS) is tasked with predicting the preferable movie to watch by utilising the ratings provided. A Dual module-deeper and more comprehensive Dense Neural Network (DNN) learning model is constructed and assessed for movie recommendation using Movie-Lens datasets containing 100k and 1M ratings on a scale of 1 to 5. The model incorporates categorical and numerical features by utilising embedding and dense layers. The improved DNN is constructed using various optimizers such as Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam), along with the implementation of dropout. The utilisation of the Rectified Linear Unit (ReLU) as the activation function in dense neural networks is employed to mitigate issues such as overfitting, gradient instability, and convergence deceleration. The Dense NN model that has been proposed is subjected to a comparative analysis with pre-existing models. The evaluation of the proposed model is conducted using two widely accepted metrics, namely Mean Squared Error (MSE) and Mean Absolute Error (MAE). The obtained results for the MSE and MAE are 0.16 and 0.33, respectively.