Deep Learning Based Recommender System: A Survey and New Perspectives
Deep Learning Based Recommender System: A Survey and New Perspectives
复制标题
基于深度学习的推荐系统：综述与新视角
DOI：
10.1145/3285029
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发表时间：
2019-02-01
影响因子：
16.6
通讯作者：
Tay, Yi
中科院分区：
文献类型：
--
作者：
Zhang, Shuai;Yao, Lina;Tay, Yi
随着网络信息量的不断增长，推荐系统已经成为克服信息过载的有效策略。推荐系统的效用不能被夸大，因为它们在许多网络应用程序中被广泛采用，沿着它们的潜在影响，以改善与过度选择相关的许多问题。近年来，深度学习在计算机视觉和自然语言处理等许多研究领域引起了相当大的兴趣，这不仅是因为它具有出色的性能，而且还因为从头开始学习特征表示的吸引力。深度学习的影响也很普遍，最近证明了它在应用于信息检索和推荐系统研究时的有效性。推荐系统中的深度学习领域正在蓬勃发展。本文旨在全面回顾基于深度学习的推荐系统的最新研究成果。更具体地说，我们提供并设计了基于深度学习的推荐模型的分类，沿着对最新技术进行了全面总结。最后，我们扩展了当前的趋势，并提供了与该领域这一令人兴奋的新发展相关的新观点。
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.