With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.
翻译:随着在线数据数量的指数级增长,搜索和查找所需信息已成为繁琐而耗时的任务。推荐系统作为信息检索和决策支持系统中的一个子类,通过提供个性化建议帮助用户更高效地访问所需信息。在构建推荐系统的不同技术中,协同过滤(CF)是最流行和广泛应用的方法。然而,冷启动和数据稀疏是实现有效的基于CF的推荐系统的基本挑战。最近成功的深度学习架构的发展激励了许多研究提出基于深度学习的解决方案来解决推荐系统的弱点。在这项研究中,与过去涵盖不同技术的类似工作不同,我们专门提供深度学习-based的协同过滤推荐系统的全面审查。这种深入过滤给出了一个关于利用深度学习技术构建CF系统作为最有影响力的推荐者的受欢迎程度,空白和被忽视的领域的清晰概述。