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：随着在线数据呈指数级增长，搜索和查找所需信息已成为一项广泛而耗时的任务。推荐系统作为信息检索和决策支持系统的子类，通过提供个性化建议帮助用户更高效地获取所需内容。在构建推荐系统的不同技术中，协同过滤（CF）是最流行且广泛应用的方法。然而，冷启动和数据稀疏性是实施有效基于CF的推荐系统所面临的基本挑战。近年来，深度学习的增强与实现取得了成功发展，促使许多研究提出基于深度学习的解决方案来弥补推荐系统的弱点。与以往关于在推荐系统中使用深度学习架构的研究通常涵盖不同技术的一般性讨论不同，本研究专门对基于深度学习的协同过滤推荐系统进行全面综述。这种深度筛选清晰地展示了利用深度学习技术构建最具影响力的CF推荐系统在流行程度、研究空白及被忽视领域方面的现状。