Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or descriptions. Recent advances in Large Language Models (LLMs) have inspired a surge of research that encodes item textual information with high-dimensional semantic embeddings, and designs transformation methods to inject such embeddings into SR models. These embedding transformation strategies can be categorized into two types, both of which exhibits notable drawbacks: 1) adapter-based methods suffer from pronounced dimension collapse, concentrating information into a few dominant dimensions; 2) SVD-based methods are rigid and manual, considering only a few principal spectral components while discarding rich information in the remaining spectrum. To address these limitations, we propose SpecTran, a spectral-aware transformer-based adapter that operates in the spectral domain, attending to the full spectrum to select and aggregates informative components. A learnable spectral-position encoding injects singular-value cues as an inductive bias, guiding attention toward salient spectral components and promoting diversity across embedding dimensions. Across four real-world datasets and three SR backbones, it consistently outperforms strong baselines, achieving an average improvement of 9.17%.

翻译：摘要：传统序列推荐模型从用户-物品交互中学习低维物品ID嵌入，往往忽视物品标题或描述等文本信息。大语言模型的最新进展激发了大量研究，通过高维语义嵌入编码物品文本信息，并设计转换方法将这些嵌入注入序列推荐模型。现有嵌入转换策略可分为两类，均存在显著缺陷：(1)基于适配器的方法出现明显维度塌缩，信息集中在少数主导维度；(2)基于奇异值分解的方法僵化且需手动处理，仅考虑少数主频谱分量，丢弃剩余频谱中的丰富信息。针对这些局限，我们提出SpecTran——一种频谱感知的Transformer适配器，它在频谱域中运作，关注完整频谱以选择并聚合信息丰富的分量。可学习的频谱位置编码将奇异值线索作为归纳偏置注入，引导注意力聚焦显著频谱分量，促进嵌入维度多样性。在四个真实数据集和三个序列推荐主干模型上，该方案持续超越强基线方法，平均提升9.17%。