In practical scenarios, the effectiveness of sequential recommendation systems is hindered by the user cold-start problem, which arises due to limited interactions for accurately determining user preferences. Previous studies have attempted to address this issue by combining meta-learning with user and item-side information. However, these approaches face inherent challenges in modeling user preference dynamics, particularly for "minor users" who exhibit distinct preferences compared to more common or "major users." To overcome these limitations, we present a novel approach called ClusterSeq, a Meta-Learning Clustering-Based Sequential Recommender System. ClusterSeq leverages dynamic information in the user sequence to enhance item prediction accuracy, even in the absence of side information. This model preserves the preferences of minor users without being overshadowed by major users, and it capitalizes on the collective knowledge of users within the same cluster. Extensive experiments conducted on various benchmark datasets validate the effectiveness of ClusterSeq. Empirical results consistently demonstrate that ClusterSeq outperforms several state-of-the-art meta-learning recommenders. Notably, compared to existing meta-learning methods, our proposed approach achieves a substantial improvement of 16-39% in Mean Reciprocal Rank (MRR).

翻译：在实际场景中，序列推荐系统的有效性受到用户冷启动问题的制约，该问题源于用户交互数据有限，难以准确判断其偏好。已有研究尝试通过融合元学习与用户及物品的辅助信息来解决此问题。然而，这些方法在建模用户偏好动态变化方面存在固有挑战，尤其对于偏好与主流用户或“主要用户”显著不同的“次要用户”。为克服这些局限，我们提出一种名为ClusterSeq的新方法——基于元学习聚类的序列推荐系统。ClusterSeq利用用户序列中的动态信息提升物品预测精度，即便在缺少辅助信息的情况下也能发挥作用。该模型能够保留次要用户的偏好，避免其被主要用户掩盖，并充分利用同一聚类内用户的集体知识。在多个基准数据集上开展的大量实验验证了ClusterSeq的有效性。实证结果一致表明，ClusterSeq的性能优于多个当前最先进的元学习推荐器。值得注意的是，与现有元学习方法相比，我们提出的方法在平均倒数排名（MRR）指标上实现了16-39%的显著提升。