Effective representation learning in sequential recommendation systems is pivotal for precisely capturing user interaction patterns and enhancing recommendation accuracy. Nonetheless, current methodologies largely focus on item-to-item transitions, frequently overlooking the time intervals between interactions, which are integral to understanding behavior pattern shifts. Moreover, critical interaction attributes like item frequency are often neglected. Our research indicates that sequences with more consistent time intervals and items with higher interaction frequency result in superior predictive performance. In contrast, sequences with non-uniform intervals contribute to user interest drift, and infrequently interacted items are challenging to model due to sparse data, posing unique challenges that existing methods fail to adequately address. In this study, we introduce UFRec, an innovative bidirectional enhancement method for sequential recommendations. UFRec harnesses sequence uniformity and item frequency to boost performance, particularly improving the representation of non-uniform sequences and less-frequent items. These two components synergistically enhance each other, driving holistic performance optimization in intricate sequential recommendation scenarios. Additionally, we introduce a multidimensional time module to further augment adaptability. To the best of our knowledge, UFRec is the pioneering method to exploit the properties of uniformity and frequency for feature augmentation. Through comparisons with eleven state-of-the-art models across four datasets, we demonstrate that UFRec significantly surpasses current leading models.

翻译：在序列推荐系统中，有效的表示学习对于精确捕捉用户交互模式、提升推荐准确性至关重要。然而，现有方法主要关注物品间的转移关系，往往忽略了交互间的时间间隔，而时间间隔对于理解行为模式变化是不可或缺的。此外，关键的交互属性如物品频率也常被忽视。我们的研究表明，具有更一致时间间隔的序列以及交互频率更高的物品能带来更优的预测性能。相反，时间间隔不均匀的序列会导致用户兴趣漂移，而交互频率低的物品由于数据稀疏难以建模，这些独特挑战是现有方法未能充分解决的。本研究提出UFRec，一种创新的双向增强序列推荐方法。UFRec利用序列的均匀性和物品频率来提升性能，尤其改善了非均匀序列和低频物品的表示。这两个组件相互协同增强，共同推动复杂序列推荐场景下的整体性能优化。此外，我们引入了一个多维时间模块以进一步增强适应性。据我们所知，UFRec是首个利用均匀性和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前沿模型进行比较，我们证明UFRec显著超越了当前领先模型。