Providing prediction reliability through deep neural networks for recommender systems

Deep learning-based recommendation approaches have shown significant improvement in the accuracy of recommender systems (RSs). However, beyond accuracy, reliability measures are gaining attention to evaluate the validity of predictions and enhance user satisfaction. Such measures can ensure that the recommended items are high-scoring items with high reliability. To integrate the native concept of reliability into a deep learning model, this paper proposes a deep neural network-based recommendation framework with prediction reliability. This framework filters out unreliable prediction ratings according to a pre-defined reliability threshold, ensuring the credibility and reliability of top-N recommendation. The proposed framework relies solely on user ratings for reliability, making it highly generalizable and scalable. Additionally, we design a data pre-processing method to address the issue of uneven distribution of ratings before model training, which effectively improves the effectiveness and fairness. The experiments on four benchmark datasets demonstrate that the proposed scheme is superior to other comparison methods in evaluation metrics. Furthermore, our framework performs better on sparse datasets than on dense datasets, indicating its ability to make strong predictions even with insufficient information.