The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.

中文翻译：

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顺序推荐系统：挑战、进展和前景

近年来，序列推荐系统这一新兴话题引起了越来越多的关注。 与包括协同过滤和基于内容的过滤在内的传统推荐系统不同，SRS 试图理解和建模序列用户行为、用户与项目之间的交互以及用户偏好和项目流行度随时间的演变。SRSs 涉及以上几个方面，以便更精确地表征用户上下文、意图和目标以及物品消费趋势，从而产生更准确、定制化和动态的推荐。本文对 SRSs 进行了系统回顾。我们首先介绍了 SRSs 的特点。 SRS，然后总结和分类该研究领域的关键挑战，