  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.
