SeGMA4Rec: Learning stable sequential representations through semantic graph propagation and hierarchical contrastive alignment

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Deep sequential encoders such as Transformers are widely used for modeling user behavior in sequential recommendation, yet their effectiveness can be limited when interaction sequences are short and sparse. In such settings, insufficient behavioral evidence makes it difficult to learn reliable representations. Existing contrastive learning methods provide useful auxiliary supervision, but they often rely on random sequence perturbations that may remove informative interactions and typically apply contrastive objectives only to the final encoder layer. Moreover, interaction-based models cannot fully capture global semantic relations among items that rarely or never co-occur. To address these limitations, we propose Semantic Global Graph with Multi-layer Alignment for Recommendation (SeGMA4Rec), which combines item-level semantic enrichment with sequence-level hierarchical regularization. SeGMA4Rec constructs a semantic item graph from pretrained textual embeddings, adaptively refines the retained edge weights, and propagates task-relevant information among related items. The graph-enhanced item representations are then processed by a sequential encoder whose attention scores guide probabilistic view generation toward retaining informative interactions. Three contrastive objectives further align representations across augmented views and encoder layers, providing direct regularization at multiple depths. Experiments on four benchmark datasets show that SeGMA4Rec achieves strong and generally superior performance over competitive baselines, including improvements of up to 9.9% in Recall@10 and 8.1% in NDCG@10 on ML-1M. Additional analyses confirm the complementary contributions of semantic graph propagation, adaptive edge refinement, attention-guided augmentation, and multi-layer contrastive alignment.