  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.
