Representation learning in sequential recommendation is critical for accurately modeling user interaction patterns and improving recommendation precision. However, existing approaches predominantly emphasize item-to-item transitions, often neglecting the time intervals between interactions, which are closely related to behavior pattern changes. Additionally, broader interaction attributes, such as item frequency, are frequently overlooked. We found that both sequences with more uniform time intervals and items with higher frequency yield better prediction performance. Conversely, non-uniform sequences exacerbate user interest drift and less-frequent items are difficult to model due to sparse sampling, presenting unique challenges inadequately addressed by current methods. In this paper, we propose UniRec, a novel bidirectional enhancement sequential recommendation method. UniRec leverages sequence uniformity and item frequency to enhance performance, particularly improving the representation of non-uniform sequences and less-frequent items. These two branches mutually reinforce each other, driving comprehensive performance optimization in complex sequential recommendation scenarios. Additionally, we present a multidimensional time module to further enhance adaptability. To the best of our knowledge, UniRec is the first method to utilize the characteristics of uniformity and frequency for feature augmentation. Comparing with eleven advanced models across four datasets, we demonstrate that UniRec outperforms SOTA models significantly. The code is available at https://github.com/Linxi000/UniRec.

翻译：序列推荐中的表示学习对于准确建模用户交互模式及提升推荐精度至关重要。然而，现有方法主要侧重于物品间的转移关系，往往忽略了交互之间的时间间隔，而时间间隔与行为模式的变化密切相关。此外，更广泛的交互属性（如物品频率）也常被忽视。我们发现，具有更均匀时间间隔的序列以及更高频率的物品均能带来更好的预测性能。反之，非均匀序列会加剧用户兴趣漂移，而低频物品由于采样稀疏难以建模，这些独特挑战在当前方法中未能得到充分解决。本文提出UniRec，一种新颖的双向增强序列推荐方法。UniRec利用序列均匀性和物品频率来提升性能，特别改善了非均匀序列和低频物品的表示效果。这两个分支相互增强，共同推动复杂序列推荐场景下的综合性能优化。此外，我们提出了一个多维时间模块以进一步增强适应性。据我们所知，UniRec是首个利用均匀性与频率特性进行特征增强的方法。通过在四个数据集上与十一种先进模型进行比较，我们证明UniRec显著优于当前最优（SOTA）模型。代码发布于https://github.com/Linxi000/UniRec。