提出结构保持投影方法，缓解大模型推荐中的模态偏差问题
Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation
- 设计结构保持损失函数，保留协同信号的几何关系
- 在多个数据集上提升推荐性能，验证方法有效性
- 适合关注大模型推荐鲁棒性的研究者和工程师
近年来基于大模型的推荐系统通过将协同嵌入投影到大模型的嵌入空间来融合文本与协同信号。然而，这种投影可能引入模态偏差，扭曲协同信号的底层结构，降低投影嵌入的实用性。为解决此问题，我们提出一种新型的结构保持投影方法，通过专用的结构保持损失函数，保留协同嵌入的相对关系。大量实验表明，该方法在多个基准数据集上均能稳定提升推荐性能，为基于大模型的推荐提供了更可靠的路径。
原文摘要 · Abstract (English)
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.
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