A novel deep multi-criteria collaborative filtering model for recommendation system

Recommender systems have been in existence everywhere with most of them using single ratings in prediction. However, multi-criteria predictions have been proved to be more accurate. Recommender systems have many techniques; collaborative filtering is one of the most commonly used. Deep learning has achieved impressive results in many domains such as text, voice, and computer vision. Lately, deep learning for recommender systems began to gain massive interest, and many recommendation models based on deep learning have been proposed. However, as far as we know, there is not yet any study which gathers multi-criteria recommendation and collaborative filtering with deep learning. In this work, we propose a novel multi-criteria collaborative filtering model based on deep learning. Our model contains two parts: in the first part, the model obtains the users and items' features and uses them as an input to the criteria ratings deep neural network, which predicts the criteria ratings. Those criteria ratings constitute the input to the second part, which is the overall rating deep neural network and is used to predict the overall rating. Experiments on a real- world dataset demonstrate that our proposed model outperformed the other state-of-the-art methods, and this provides evidence pointing to the success of employing deep learning and multi-criteria in recommendation systems. (C) 2019 Elsevier B.V. All rights reserved.