面向基于大语言模型（LLM）的序列推荐中缓解模态偏差的结构保留投影
Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation
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中文总结 AI 辅助
本文针对基于LLM的序列推荐中投影引入的模态偏差问题，提出结构保留投影方法，通过专用损失维持协同嵌入关系几何，经实验验证可提升推荐性能。
中文摘要 AI 辅助
现有的基于大语言模型（LLM）的推荐系统通过将协同嵌入投影到LLM的嵌入空间中，整合文本信号与协同信号。但该投影会引入模态偏差，扭曲底层协同结构，降低投影嵌入的可用性。为解决此问题，本文提出一种新颖的结构保留投影方法，通过专用的结构保留损失维持协同嵌入的关系几何结构。综合实验表明，该方法可持续提升推荐性能，为基于LLM的推荐提供更可靠的路径。
英文摘要
Recent LLM-based recommenders integrate textual and collaborative signals by projecting collaborative embeddings into the embedding space of the LLM. However, this projection can introduce modality bias that distorts the underlying collaborative structure and limits the usefulness of projected embeddings. To address this issue, we propose a novel structure-preserving projection approach that maintains the relational geometry of collaborative embeddings through dedicated structure-preserving losses. Comprehensive experiments demonstrate that our approach consistently improves recommendation performance, providing a more reliable path for LLM-based recommendation.
