June 18, 2024

Sajad Ahmadian

Academic rank: Assistant professor
Education: Ph.D in Computer Engineering
Phone: 09188339565
Faculty: Faculty of Information Technology


DHSIRS: a novel deep hybrid side information-based recommender system
Type Article
Recommender system; Deep neural networks; Data sparsity; Dot-product; Side information; Latent feature representation
Researchers Amir Khani Yengikand، Majid Meghdadi، Sajad Ahmadian


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.