  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).
