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

As a pivotal extension of the renowned ChatGPT, the GPT Store serves as a
dynamic marketplace for various Generative Pre-trained Transformer (GPT)
models, shaping the frontier of conversational AI. This paper presents an
in-depth measurement study of the GPT Store, with a focus on the categorization
of GPTs by topic, factors influencing GPT popularity, and the potential
security risks. Our investigation starts with assessing the categorization of
GPTs in the GPT Store, analyzing how they are organized by topics, and
evaluating the effectiveness of the classification system. We then examine the
factors that affect the popularity of specific GPTs, looking into user
preferences, algorithmic influences, and market trends. Finally, the study
delves into the security risks of the GPT Store, identifying potential threats
and evaluating the robustness of existing security measures. This study offers
a detailed overview of the GPT Store's current state, shedding light on its
operational dynamics and user interaction patterns. Our findings aim to enhance
understanding of the GPT ecosystem, providing valuable insights for future
research, development, and policy-making in generative AI.

这篇论文通过深入测量研究 GPT Store，重点关注 GPT 的分类、影响 GPT 流行度的因素以及潜在的安全风险，旨在提供 GPT Store 的当前状态的详细概述，揭示其运营动态和用户互动模式，为未来生成 AI 的研究、发展和政策制定提供宝贵的洞见。