Recommender systems (RSs) have been employed for many real-world applications including search
engines, social networks, and information retrieval systems as powerful intelligent techniques for
creating personalized recommendations. RSs mainly utilize different data resources such as user-item
ratings, tag data, and social relationships in their recommendation process to overcome the data
sparsity issue. One of the main challenges is how to integrate the results obtained by these data
resources. Moreover, most of the existing RSs focus on the recommendation accuracy, and do not
consider the reliability of the recommendations. To address these challenges, in this study, a reliable
recommendation method is developed, which employs deep neural networks and reinforcement
learning. The proposed method firstly uses a stacked denoising autoencoder model to obtain the latent
features from the rating, tag, and trust data. The similarity value between each pair of users is then
calculated in three different aspects according to the extracted latent features. An ensemble approach
is developed by integrating the three similarities using reinforcement learning to determine the final
similarity value. The ensemble similarity values are used in the rating prediction process, and the
reliability of the predicted ratings is evaluated using an effective measure. Finally, a recommendation
strategy is developed based on the integration of the predicted ratings and their reliability values. We
evaluated the effectiveness of the proposed method by conducting numerous experiments on three
datasets where the results show that the proposed method is far superior to other recommendation
models in making more accurate recommendations.