These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications such as Natural Language Processing (NLP) and image processing, more efforts have been made by the researchers to exploit deep learning methods for improving the performance of RS. However, despite the several research works on deep learning based RS, very few secondary studies were conducted in the field. Therefore, this study aims to provide a systematic literature review (SLR) of deep learning based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the field. This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications. The paper particularly adopts an SLR approach based on the standard guidelines of the SLR designed by Kitchemen-ham which uses selection method and provides detail analysis of the research publications. Several publications were gathered and after inclusion/exclusion criteria and the quality assessment, the selected papers were finally used for the review. The results of the review indicated that autoencoder (AE) models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs) models. Also, the results showed that Movie Lenses is the most popularly used datasets for the deep learning-based RS evaluation followed by the Amazon review datasets. Based on the results, the movie and e-commerce have been indicated as the most common domains for RS and that precision and Root Mean Squared Error are the most commonly used metrics for evaluating the performance of the deep leaning based RSs.

中文翻译：

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基于深度学习方法的推荐系统：系统回顾和新方向

如今，许多推荐系统 (RS) 被用于解决电子商务、娱乐和社交媒体等领域的信息过载问题。尽管 RS 的经典方法在提供项目推荐方面取得了显着的成功，但它们仍然存在冷启动和数据稀疏等诸多问题。随着深度学习在自然语言处理 (NLP) 和图像处理等各种应用中的最新成就，研究人员做出了更多努力来利用深度学习方法来提高 RS 的性能。然而，尽管对基于深度学习的 RS 进行了多项研究工作，但在该领域进行的二次研究很少。所以，本研究旨在提供基于深度学习的 RSs 的系统文献综述 (SLR)，可以指导研究人员和从业人员更好地了解该领域的新趋势和挑战。本文是第一篇专门针对基于深度学习的 RS 的 SLR，根据最优质的研究出版物总结和分析现有研究。本文特别采用了基于Kitchemen-ham 设计的SLR 标准指南的SLR 方法，该方法使用选择方法并提供对研究出版物的详细分析。收集了几篇出版物，经过纳入/排除标准和质量评估，最终将选定的论文用于审查。审查结果表明，自动编码器 (AE) 模型是应用最广泛的 RS 深度学习架构，其次是卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型。此外，结果表明 Movie Lenses 是基于深度学习的 RS 评估最常用的数据集，其次是亚马逊评论数据集。根据结果​​，电影和电子商务已被指出是 RS 最常见的领域，精度和均方根误差是评估基于深度学习的 RS 性能的最常用指标。结果表明，电影镜头是基于深度学习的 RS 评估最常用的数据集，其次是亚马逊评论数据集。根据结果​​，电影和电子商务已被指出是 RS 最常见的领域，精度和均方根误差是评估基于深度学习的 RS 性能的最常用指标。结果表明，电影镜头是基于深度学习的 RS 评估最常用的数据集，其次是亚马逊评论数据集。根据结果​​，电影和电子商务已被指出是 RS 最常见的领域，精度和均方根误差是评估基于深度学习的 RS 性能的最常用指标。