  Modern recommender systems are required to adapt to the change in user
preferences and item popularity. Such a problem is known as the temporal
dynamics problem, and it is one of the main challenges in recommender system
modeling. Different from the popular recurrent modeling approach, we propose a
new solution named LeapRec to the temporal dynamic problem by using
trajectory-based meta-learning to model time dependencies. LeapRec
characterizes temporal dynamics by two complement components named global time
leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term
patterns by finding the shortest learning path across unordered temporal data.
Cooperatively, OTL learns short-term patterns by considering the sequential
nature of the temporal data. Our experimental results show that LeapRec
consistently outperforms the state-of-the-art methods on several datasets and
recommendation metrics. Furthermore, we provide an empirical study of the
interaction between GTL and OTL, showing the effects of long- and short-term
modeling.
