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 this https URL

UniRec 是一个新颖的双向增强顺序推荐方法，利用序列一致性和物品频率来提高性能，特别是在表示不均匀序列和低频物品方面；与其他先进模型相比，在四个数据集上表现显著优越。

UniRec：顺序推荐中统一性和频度的双重提升

UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential  Recommendations

Differential Privacy (DP) provides strong guarantees on the risk of
compromising a user's data in statistical learning applications, though these
strong protections make learning challenging and may be too stringent for some
use cases. To address this, we propose element level differential privacy,
which extends differential privacy to provide protection against leaking
information about any particular "element" a user has, allowing better utility
and more robust results than classical DP. By carefully choosing these
"elements," it is possible to provide privacy protections at a desired
granularity. We provide definitions, associated privacy guarantees, and
analysis to identify the tradeoffs with the new definition; we also develop
several private estimation and learning methodologies, providing careful
examples for item frequency and M-estimation (empirical risk minimization) with
concomitant privacy and utility analysis. We complement our theoretical and
methodological advances with several real-world applications, estimating
histograms and fitting several large-scale prediction models, including deep
networks.

本文提出了元素级差分隐私的概念，以更好地保护个人数据并维持数据分析的实用性；研究者提供了定义和分析，并开发了几种私人估计和学习方法，并在多种实际应用中进行了验证。