Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibit sparse interactions, known as the tail-item problem. This issue limits the model's ability to accurately capture item transition patterns. To tackle this, large language models (LLMs) offer a promising solution by capturing semantic relationships between items. Despite previous efforts to leverage LLM-derived embeddings for enriching tail items, they still face the following limitations: 1) They struggle to effectively fuse collaborative signals with semantic knowledge, leading to suboptimal item embedding quality. 2) Existing methods overlook the structural inconsistency between the ID and LLM embedding spaces, causing conflicting signals that degrade recommendation accuracy. In this work, we propose a Fusion and Alignment Enhancement framework with LLMs for Tail-item Sequential Recommendation (FAERec), which improves item representations by generating coherently-fused and structurally consistent embeddings. For the information fusion challenge, we design an adaptive gating mechanism that dynamically fuses ID and LLM embeddings. Then, we propose a dual-level alignment approach to mitigate structural inconsistency. The item-level alignment establishes correspondences between ID and LLM embeddings of the same item through contrastive learning, while the feature-level alignment constrains the correlation patterns between corresponding dimensions across the two embedding spaces. Furthermore, the weights of the two alignments are adjusted by a curriculum learning scheduler to avoid premature optimization of the complex feature-level objective. Extensive experiments across three widely used datasets with multiple representative SR backbones demonstrate the effectiveness and generalizability of our framework.

翻译：序列推荐（Sequential Recommendation, SR）通过学习用户的历史交互序列来理解其偏好，并提供个性化推荐。在实际场景中，大多数物品交互稀疏，即存在长尾物品问题。该问题限制了模型准确捕捉物品间转换模式的能力。为此，大语言模型（Large Language Models, LLMs）通过捕获物品间的语义关系提供了一种有前景的解决方案。尽管已有研究尝试利用LLM生成的嵌入向量来丰富长尾物品，但它们仍面临以下局限：1）难以有效融合协同信号与语义知识，导致物品嵌入质量欠佳；2）现有方法忽略了ID嵌入空间与LLM嵌入空间之间的结构不一致性，产生冲突信号从而降低推荐准确性。本文提出了一种基于大语言模型的长尾物品序列推荐融合与对齐增强框架（FAERec），通过生成连贯融合且结构一致的嵌入向量来改进物品表示。针对信息融合挑战，我们设计了一种自适应门控机制以动态融合ID嵌入与LLM嵌入。随后，提出了一种双层对齐方法以缓解结构不一致性：物品级对齐通过对比学习建立同一物品在ID与LLM嵌入空间之间的对应关系，而特征级对齐则约束两个嵌入空间中对应维度之间的相关性模式。此外，通过课程学习调度器动态调整两种对齐的权重，以避免对复杂的特征级目标过早优化。在三个广泛使用的数据集上，以多种代表性序列推荐模型为骨干网络进行的大量实验，验证了本框架的有效性与泛化能力。