Leaping through Time with Gradient-Based Adaptation for Recommendation
Keywords:Machine Learning (ML)
AbstractModern 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.
How to Cite
Chairatanakul, N., NT, H., Liu, X., & Murata, T. (2022). Leaping through Time with Gradient-Based Adaptation for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6141-6149. https://doi.org/10.1609/aaai.v36i6.20562
AAAI Technical Track on Machine Learning I