Leaping Through Time with Gradient-based Adaptation for Recommendation
Leaping Through Time with Gradient-based Adaptation for Recommendation
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DOI：
10.1609/aaai.v36i6.20562
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发表时间：
2021-12
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影响因子：
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通讯作者：
Nuttapong Chairatanakul;Hoang NT;Xin Liu;T. Murata
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文献类型：
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作者：
Nuttapong Chairatanakul;Hoang NT;Xin Liu;T. Murata
现代推荐系统需要适应用户偏好和项目流行度的变化。这样的问题被称为时间动态问题，它是推荐系统建模的主要挑战之一。不同于流行的递归建模方法，我们提出了一个新的解决方案命名为LeapRec的时间动态问题，使用基于语义的元学习模型的时间依赖。LeapRec通过两个互补分量来表征时间动态，称为全局时间跳跃（GTL）和有序时间跳跃（OTL）。通过设计，GTL通过在无序的时态数据中找到最短的学习路径来学习长期模式。合作，OTL学习短期模式，考虑时序数据的连续性。我们的实验结果表明，LeapRec在几个数据集和推荐指标上始终优于最先进的方法。此外，我们提供了GTL和OTL之间的相互作用的实证研究，显示长期和短期建模的影响。
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.