TY - JOUR AU - Chairatanakul, Nuttapong AU - NT, Hoang AU - Liu, Xin AU - Murata, Tsuyoshi PY - 2022/06/28 Y2 - 2024/03/28 TI - Leaping through Time with Gradient-Based Adaptation for Recommendation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 6 SE - AAAI Technical Track on Machine Learning I DO - 10.1609/aaai.v36i6.20562 UR - https://ojs.aaai.org/index.php/AAAI/article/view/20562 SP - 6141-6149 AB - 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. ER -