  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.
