The development of Internet comes up with the prosperity of E-commerce all over the world. In order to promote sales and save consumers’ labor in commodity browsing, recommender systems are proposed by E-commerce platforms to provide online consumers with products and services of their potential interests. The primary challenge in recommendation roots in the intricacy in quantifying users’ preferences on items with the reality of data sparsity and the computation complexity. Hence, more and more researchers are attempting deep learning techniques to deal with the challenge with the hope of using advanced algorithms to alleviate the intricacy. Word embedding is used to learn the association of items in a space of low dimensionality. Multi-layer perception is used to learn users’ preferences on items in a data-driven manner with a customized loss function. The future work of recommender systems includes three folds. The one is to make use of multi-source data to combine implicit and explicit user behavior data to address the problem of data sparsity. The second is dynamic recommendation with the changing users’ preferences on items and make recommender systems light-weight and useable in complex scenarios. The third is to provide effective and verifiable recommendation under the premise of user privacy protection

互联网的发展伴随着全球电子商务的繁荣。为了促进销售和节省消费者浏览商品的劳动，电子商务平台提出了推荐系统，为在线消费者提供他们潜在兴趣的产品和服务。推荐的主要挑战源于在数据稀疏和计算复杂性的现实下量化用户对项目的偏好的复杂性。因此，越来越多的研究人员正在尝试使用深度学习技术来应对挑战，希望使用先进的算法来减轻复杂性。词嵌入用于学习低维空间中项目的关联。多层感知用于以数据驱动的方式通过定制的损失函数来学习用户对物品的偏好。推荐系统的未来工作包括三个方面。一是利用多源数据，结合隐式和显式用户行为数据，解决数据稀疏问题。第二个是动态推荐，随着用户对物品的偏好不断变化，使推荐系统重量轻，可用于复杂场景。三是在保护用户隐私的前提下，提供有效且可验证的推荐 第二个是动态推荐，随着用户对物品的偏好不断变化，使推荐系统重量轻，可用于复杂场景。三是在保护用户隐私的前提下，提供有效且可验证的推荐 第二个是动态推荐，随着用户对物品的偏好不断变化，使推荐系统重量轻，可用于复杂场景。三是在保护用户隐私的前提下，提供有效且可验证的推荐