Edge networking is a complex and dynamic computing paradigm that aims to push cloud re-sources closer to the end user improving responsiveness and reducing backhaul traffic. User mobility, preferences, and content popularity are the dominant dynamic features of edge networks. Temporal and social features of content, such as the number of views and likes are leveraged to estimate the popularity of content from a global perspective. However, such estimates should not be mapped to an edge network with particular social and geographic characteristics. In next generation edge networks, i.e., 5G and beyond 5G, machine learning techniques can be applied to predict content popularity based on user preferences, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network. This article investigates the application of machine learning techniques for in-network caching in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (method, objective, and features), (b) caching strategy (policy, location, and replacement), and (c) edge network (type and delivery strategy). A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning in edge networks.

边缘网络是一种复杂而动态的计算范例，旨在将云资源推向更接近最终用户的位置，从而提高响应速度并减少回程流量。用户移动性，偏好和内容受欢迎程度是边缘网络的主要动态特征。内容的时间和社交特征（例如，观看次数和喜欢的次数）被用来从全球角度评估内容的受欢迎程度。但是，此类估计不应映射到具有特定社会和地理特征的边缘网络。在下一代边缘网络（即5G和5G以及更高的网络）中，机器学习技术可以应用于根据用户偏好来预测内容流行度，根据相似的内容兴趣来对用户进行聚类，优化缓存的放置和替换策略提供了一组有关网络状态的约束和预测。机器学习的这些应用可以帮助识别边缘网络的相关内容。本文研究了机器学习技术在边缘网络中进行网络内缓存的应用。我们调查了最新的文献，并根据（a）机器学习技术（方法，目标和功能），（b）缓存策略（策略，位置和替换），以及（c）边缘网络（类型和交付策略）。针对分类学中确定的参数，对最新文献进行了比较分析。此外，我们还讨论了最佳缓存决策以及边缘网络中机器学习的应用的研究挑战和未来方向。