Denoising Time Cycle Modeling for Recommendation
This addresses a specific issue in recommender systems for improving recommendation accuracy by modeling diverse time cycles, but it appears incremental as it builds on existing temporal modeling approaches.
The paper tackles the problem of irrelevant user behaviors (noises) in temporal recommendation by proposing DiCycle to denoise and select target-related behaviors, resulting in superior performance over state-of-the-art methods on public benchmarks and a real-world dataset.
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.