UFRec: Integrating Uniformity and Frequency to Enhance Sequential Recommendations

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.