Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria recommender systems proved to have more accurate recommendations compared to single-criterion recommender systems as multi-criteria rating reflects the user appreciation of an item in terms of many aspects. On the another hand, deep learning techniques achieve promising performance in many research areas such as image processing, computer vision, pattern recognition and natural language processing. Recently, the application of deep learning in recommender systems have been frequently explored with encouraging results. Accordingly, this paper proposes a deep learning based algorithm for multi-criteria recommender systems in which deep autoencoders are employed to exploit the non-trivial, nonlinear and hidden relations between users with regard to multi-criteria preferences, and generate more accurate recommendations. Experiments on the Yahoo! Movies and TripAdvisor multi-criteria datasets show that the proposed algorithm prove to be very effective in terms of producing more accurate predictions compared with the state-of-the-art recommendation algorithms

近年来，推荐系统通过提供个性化的建议而异常广泛地用于处理信息过载问题。与单标准推荐器系统相比，多标准推荐器系统被证明具有更准确的推荐，因为多标准评级从多个方面反映了用户对某项商品的赞赏。另一方面，深度学习技术在许多研究领域（例如图像处理，计算机视觉，模式识别和自然语言处理）取得了可喜的成绩。近来，深度学习在推荐系统中的应用已被频繁探索，并取得了令人鼓舞的结果。因此，本文针对多准则推荐系统提出了一种基于深度学习的算法，该算法采用深度自动编码器来利用用户之间关于多准则偏好的非平凡，非线性和隐藏关系，并生成更准确的推荐。在Yahoo!上进行实验 电影和TripAdvisor的多标准数据集表明，与最新的推荐算法相比，该算法在产生更准确的预测方面非常有效