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).

通过将用户序列中的动态信息与聚类相结合，ClusterSeq是一种在缺乏侧面信息的情况下提高项目预测准确性的元学习聚类型顺序推荐系统，对于展示与常见用户不同偏好的“次要用户”而言，这种模型能够保留其偏好并利用同一聚类中用户的集体知识。实验证实了ClusterSeq的有效性，相比现有的元学习方法，在均衡互惠排名(MRR)上取得了16-39%的显著改善。

ClusterSeq：基于聚类的元学习增强顺序推荐系统