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.

利用神经 ODE 模型再现用户在实际推荐系统中的重复消费行为，以捕捉时间间隔的动态模式，并结合静态偏好预测模块，对用户在特定情境下的个性化偏好进行综合建模。实验证明，该模型明显提高基准模型的性能，并优于其他对比方法。

ReCODE：用神经 ODE 模型重复消费行为

ReCODE: Modeling Repeat Consumption with Neural ODE

Recurrent neural networks for session-based recommendation have attracted a
lot of attention recently because of their promising performance. repeat
consumption is a common phenomenon in many recommendation scenarios (e.g.,
e-commerce, music, and TV program recommendations), where the same item is
re-consumed repeatedly over time. However, no previous studies have emphasized
repeat consumption with neural networks. An effective neural approach is needed
to decide when to perform repeat recommendation. In this paper, we incorporate
a repeat-explore mechanism into neural networks and propose a new model, called
RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular
neural recommendation approach in the decoder with a new repeat recommendation
mechanism that can choose items from a user's history and recommends them at
the right time. We report on extensive experiments on three benchmark datasets.
RepeatNet outperforms state-of-the-art baselines on all three datasets in terms
of MRR and Recall. Furthermore, as the dataset size and the repeat ratio
increase, the improvements of RepeatNet over the baselines also increase, which
demonstrates its advantage in handling repeat recommendation scenarios.

本论文提出了一种新模型 RepeatNet，该模型可在用户历史记录中选择项目，并在正确的时间向用户推荐以处理重复推荐场景，并在三个基准数据集上展示了超过现有基线方法的性能提升。