I. Introduction
Recommender systems have become an indispensable tool to provide personalized services for users and create opportunities for content providers to increase profits. Within online platforms, user behaviors are sequential by nature. This phenomenon facilitates a practical application scenario—sequential recommendation (a.k.a., next-item recommendation), where the goal is to predict the next interesting item based on a given sequence. In this scenario, user historical actions provide more evidence on long-term preferences, while the recently accessed items are more reflective of short-term user interests. To yield satisfactory results, how to simultaneously model long-term and short-term user preferences has been a critical issue in this task.