Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.

翻译：近期，用户-物品交互的时间模式建模在推荐系统中引起了广泛关注。我们指出，现有方法忽略了用户行为时间模式的多样性。我们将与目标物品无关的用户行为子集定义为噪声，这些噪声限制了与目标相关的时间周期建模的性能，并影响了推荐效果。本文提出了一种新颖的方法——去噪时间周期建模（DiCycle），用于对用户行为进行去噪，并筛选出与目标物品高度相关的用户行为子集。DiCycle能够显式地为推荐建模多样化的时间周期模式。通过在公共基准数据集和真实世界数据集上进行的大量实验，证明了DiCycle相较于最先进的推荐方法具有卓越的性能。