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

机译：推荐系统的新型深度多准则协同过滤模型

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摘要

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.
机译：推荐器系统随处可见，其中大多数使用单个评级进行预测。但是，多标准预测已被证明更为准确。推荐系统有很多技术。协作过滤是最常用的协作过滤之一。深度学习在文本，语音和计算机视觉等许多领域都取得了令人瞩目的成果。最近，推荐系统的深度学习开始引起人们的广泛兴趣，并且已经提出了许多基于深度学习的推荐模型。但是，据我们所知，还没有任何研究可以收集多标准推荐和深度学习的协作过滤。在这项工作中，我们提出了一种基于深度学习的新颖的多准则协作过滤模型。我们的模型包括两个部分：在第一部分中，该模型获取用户和项目的特征，并将其用作标准等级深度神经网络的输入，该神经网络预测标准等级。这些标准等级构成第二部分的输入，第二部分是总体等级深度神经网络，用于预测总体等级。在真实数据集上进行的实验表明，我们提出的模型优于其他最新方法，这为在推荐系统中采用深度学习和多准则的成功提供了证据。 （C）2019 Elsevier B.V.保留所有权利。

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