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.

用户行为的时间模式建模引起推荐系统的广泛关注。本文提出了去噪时间循环建模（DiCycle）方法，通过去除与目标项目无关的用户行为噪声，选择与目标项目高度相关的用户行为子集，并明确地建模多样化的时间循环模式来进行推荐，实验证明DiCycle在公共基准和真实世界数据集上的表现优于现有的推荐方法。

推荐算法的去噪时间循环建模