Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS). In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.

中文翻译：

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基于流的推荐系统调查

推荐系统 (RS) 已被证明是帮助用户克服信息过载的有效工具，并且在过去的二十年中，该领域取得了重大进展。尽管解决推荐问题首先需要一个易于研究和评估的公式，但目前在研究贡献和实际部署 RS 的工业应用之间存在差距。特别是，大多数 RS 旨在批量运行：它们依赖于大型静态数据集并构建仅定期更新的推荐模型。这种功能在各种设置中引入了一些限制，导致考虑更现实的设置，RS 从连续的交互流中学习。这种 RS 被构建为基于流的推荐系统 (SBRS)。在本文中，我们回顾了 SBRS，强调它们与时间感知 RS 和在线自适应学习的关系，并介绍和分类解决相应问题及其多个方面的现有工作。我们讨论了用于评估 SBRS 的方法和可以使用的改编数据集，最后我们概述了该领域的开放挑战。