Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.

中文翻译：

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融合深度神经网络和矩阵分解的多准则协同过滤推荐器

推荐系统已成为通过生成个性化预测来处理信息过载的有效策略。基于深度学习的推荐系统已经取得了不错的成绩，但是其中大多数系统都是使用单个评分的传统推荐系统。在这项工作中，我们通过结合深度神经网络和矩阵分解实现了多准则协作过滤推荐器。我们的模型包括两部分：第一部分使用深度神经网络和矩阵分解的融合模型来预测标准评分，第二部分使用深度神经网络来预测整体评分。在两个数据集（包括真实数据集）上的实验结果，