  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.
