In real-world recommender systems, such as in the music domain, repeat consumption is a common phenomenon where users frequently listen to a small set of preferred songs or artists repeatedly. The key point of modeling repeat consumption is capturing the temporal patterns between a user's repeated consumption of the items. Existing studies often rely on heuristic assumptions, such as assuming an exponential distribution for the temporal gaps. However, due to the high complexity of real-world recommender systems, these pre-defined distributions may fail to capture the intricate dynamic user consumption patterns, leading to sub-optimal performance. Drawing inspiration from the flexibility of neural ordinary differential equations (ODE) in capturing the dynamics of complex systems, we propose ReCODE, a novel model-agnostic framework that utilizes neural ODE to model repeat consumption. ReCODE comprises two essential components: a user's static preference prediction module and the modeling of user dynamic repeat intention. By considering both immediate choices and historical consumption patterns, ReCODE offers comprehensive modeling of user preferences in the target context. Moreover, ReCODE seamlessly integrates with various existing recommendation models, including collaborative-based and sequential-based models, making it easily applicable in different scenarios. Experimental results on two real-world datasets consistently demonstrate that ReCODE significantly improves the performance of base models and outperforms other baseline methods.

翻译：在现实世界的推荐系统（如音乐领域）中，重复消费是一种常见现象，用户会反复收听一小部分偏爱的歌曲或艺术家。建模重复消费的关键在于捕捉用户重复消费物品之间的时间模式。现有研究通常依赖于启发式假设，例如假设时间间隔服从指数分布。然而，由于现实世界推荐系统的高度复杂性，这些预定义的分布可能无法捕捉复杂的动态用户消费模式，导致性能欠佳。受神经常微分方程（ODE）在捕捉复杂系统动态性方面灵活性的启发，我们提出了ReCODE，这是一个新颖的模型无关框架，利用神经ODE来建模重复消费。ReCODE包含两个核心组件：用户静态偏好预测模块和用户动态重复意图建模模块。通过同时考虑即时选择和历史消费模式，ReCODE在目标场景下提供了对用户偏好的全面建模。此外，ReCODE能够无缝集成到各种现有推荐模型中，包括基于协同过滤的模型和基于序列的模型，使其易于在不同场景中应用。在两个真实世界数据集上的实验结果一致表明，ReCODE显著提升了基础模型的性能，并优于其他基线方法。