Abstract: Modern recommender systems are required to adapt to the change in popularity of items and user preferences.

Such 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.