Latent factor-based methods have been extensively employed in recommender systems to
project users and items to the same feature space and use the dot product for predicting
unknown ratings. Nevertheless, the dot product method cannot describe the various
influences of latent features. Also, it only captures the linear relations between users
and items leading to a negative impact on the efficiency of recommender systems. Deep
learning models are known as state-of-the-art techniques to deal with the non-linear
relation between user and item. In this paper, we develop a new deep hybrid recommender
system called DHSIRS using multilayer perceptron neural network to combine
side information and interaction matrix for item recommendation. Specifically, two
feature learning components are developed to extract side information-based and
interaction-based latent features. Therefore, two paralleled deep neural networks are
utilized in the side information-based feature learning part to obtain the feature vector
for users and items from side information. Moreover, the interaction-based feature
learning part obtains the latent features from the user-item matrix. Finally, we introduce
a deep learning model instead of the dot product method to predict unknown ratings by
integrating the side information-based and interaction-based latent features. Unlike other
methods that use the dot product, our method is able to efficiently learn the high-order
non-linear relations between users and items. Extensive experiments on three publicly
available datasets demonstrate that DHSIRS averagely improves the recommendation
performance by around 4.18% in comparison to the second-best model over different
evaluation metrics.