A reliable deep representation learning to improve trust-aware recommendation systems

Abstract

Deep neural networks have been extensively employed in many applications such as natural language processing and computer vision. They have attracted a lot of attention in designing recommender systems due to their recent significant achievements. These models are mainly used in recommender systems to generate the users’ latent features according to the input data such as user-item rating and trust matrixes. However, such input data are significantly sparse leading to reduce the effectiveness of deep learning models in generating the latent features. In addition, the existing deep learning based recommendation models ignore the reliability measure in their recommendation process. To address these challenges, in this paper, a trust-aware recommendation method based on deep sparse autoencoder is proposed. Specifically, an effective probabilistic model is proposed to determine how many ratings are required for each user to produce an accurate prediction. Moreover, for enhancing the rating profiles of users with insufficient ratings, an efficient mechanism is developed based on the utilization of implicit ratings. In this mechanism, the implicit ratings are selected based on a reliability measure to guarantee their effectiveness. Then, the reliable enhanced rating profiles and trust statements are considered as the input data of deep sparse autoencoder to generate the users’ latent features. Finally, a similarity function that utilizes the generated latent features is developed to obtain similarity values between users and make recommendations. Massive experiments are carried out on two datasets to assess the effectiveness of the proposed method in comparison to the-state-of-the-art models. The results of experiments demonstrate that the proposed method can significantly beat other models in producing more accurate recommendations.