SAGE: Global Semantic Alignment with LLMs for Long-Tail Sequential Recommendation

Sequential recommendation (SRS) has become a core technique for modern platforms, yet the long-tail distribution of user-item interactions poses persistent challenges. Most users interact sparsely, most items receive little exposure, and existing methods struggle with three issues: (i) collaborative sparsity, where interaction signals collapse in the tail; (ii) limited semantic exploitation, since large language models (LLMs) are mainly used for shallow, point-level embeddings; and (iii) head - tail imbalance, where gains in the tail often come at the cost of head performance. We propose Semantic Alignment with Global Embedding for Rec ommendation (SAGE-Rec), a new framework that explicitly leverages global semantic organization from LLMs for sequential recommendation. On the item side, SAGE-Rec introduces a fuzzy-membership prototype mechanism that enables tail items to inherit features from semantically related head items. On the user side, it performs alignment and distillation across semantically similar users to enrich sparse representations. At the global level, it applies lightweight regularization to balance semantic and collaborative signals, alleviating the head - tail seesaw effect. Extensive experiments across three real-world datasets and backbone models demonstrate that SAGE-Rec consistently preserves head accuracy while substantially improving recommendations for tail users and items. These results highlight global semantic alignment with LLMs as a principled solution to the long-tail dilemma in sequential recommendation. The implementation code is available for easy reproducibility https://github.com/Applied-Machine-Learning-Lab/WWW2026-SAGE-LLM.