Abstract
The emerging topic of sequential recommender
systems (SRSs) has attracted increasing attention
in recent years. Different from the conventional
recommender systems (RSs) 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