In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user's immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users' historical interactions. Building on these insights, we propose a framework that combines short- and long-term preferences to enhance recommendation performance, namely Compositions of Variant Experts (CoVE). This novel framework dynamically integrates short- and long-term preferences through the use of different specialized recommendation models (i.e., experts). Extensive experiments showcase the effectiveness of the proposed methods and ablation studies further investigate the impact of variant expert types.

翻译：在在线数字领域，推荐系统无处不在，对提升用户体验起着至关重要的作用。这些系统利用用户偏好提供个性化推荐，从而帮助用户在选择的悖论中导航。本研究聚焦于个性化序列推荐，系统不仅考虑用户即时、动态变化的会话上下文，还考虑其累积的历史行为，以提供高度相关且及时的推荐。通过对多个真实世界数据集进行的实证研究，我们观察并量化了短期（即时且瞬态的）与长期（持久且稳定的）偏好对用户历史交互的存在与影响。基于这些发现，我们提出了一个整合短期与长期偏好以提升推荐性能的框架，即变体专家组合（CoVE）。这一新颖框架通过使用不同的专门化推荐模型（即专家）动态整合短期与长期偏好。大量实验展示了所提方法的有效性，消融研究进一步探讨了不同类型专家的影响。