Aspect-based recommendation methods extract aspect terms from reviews, such as price, to model fine-grained user preferences on items, making them a critical approach in personalized recommender systems. Existing methods utilize graphs to represent the relationships among users, items, and aspect terms, modeling user preferences based on graph neural networks. However, they overlook the dynamic nature of user interests - users may temporarily focus on aspects they previously paid little attention to - making it difficult to assign accurate weights to aspect terms for each user-item interaction. In this paper, we propose a long-short-term aspect interest Transformer (LSA) for aspect-based recommendation, which effectively captures the dynamic nature of user preferences by integrating both long-term and short-term aspect interests. Specifically, the short-term interests model the temporal changes in the importance of recently interacted aspect terms, while the long-term interests consider global behavioral patterns, including aspects that users have not interacted with recently. Finally, LSA combines long- and short-term interests to evaluate the importance of aspects within the union of user and item aspect neighbors, therefore accurately assigns aspect weights for each user-item interaction. Experiments conducted on four real-world datasets demonstrate that LSA improves MSE by 2.55% on average over the best baseline.

翻译：方面推荐方法通过提取评论中的方面词（如价格）来建模用户对物品的细粒度偏好，已成为个性化推荐系统中的关键方法。现有方法利用图结构表示用户、物品及方面词之间的关系，并基于图神经网络建模用户偏好。然而，这些方法忽视了用户兴趣的动态性——用户可能暂时关注过去很少注意的方面——这使得为每次用户-物品交互分配准确的方面权重变得困难。本文提出一种用于方面推荐的长短期方面兴趣Transformer（LSA），通过整合长期与短期方面兴趣有效捕捉用户偏好的动态性。具体而言，短期兴趣建模近期交互方面词重要性的时序变化，而长期兴趣则考虑全局行为模式（包括用户近期未交互的方面）。最终，LSA结合长短期兴趣评估用户与物品方面邻居并集中的方面重要性，从而为每次用户-物品交互精确分配方面权重。在四个真实数据集上的实验表明，LSA相比最优基线方法在MSE指标上平均提升2.55%。