用多模态融合去噪，提升大模型生成推荐特征的准确性
DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems
- 将降维与推荐任务联合训练，让降维过程感知最终排序目标
- 引入对比学习，利用协同过滤信号优化隐空间表示
- 适合研究大模型与推荐系统融合的开发者参考
现代推荐系统难以有效利用大语言模型（LLMs）生成的丰富但高维且含噪的多模态特征。将这些特征视为静态输入会使其与核心推荐任务脱节。本文提出一种新框架，核心思想是深度融合多模态与协同知识以实现表征去噪。统一架构包含两项关键技术：一是将维度压缩直接嵌入推荐模型，实现端到端联合训练，使降维过程感知最终排序目标；二是引入对比学习目标，显式将协同过滤信号融入隐空间。该协同过程能净化原始LLM嵌入，滤除噪声并增强任务相关信号。大量实验验证了本方法在判别能力上的优越性，证明这种集成融合与去噪策略对实现顶尖性能至关重要。本工作为有效利用LLMs于推荐系统提供了基础范式。
原文摘要 · Abstract (English)
Modern recommender systems struggle to effectively utilize the rich, yet high-dimensional and noisy, multi-modal features generated by Large Language Models (LLMs). Treating these features as static inputs decouples them from the core recommendation task. We address this limitation with a novel framework built on a key insight: deeply fusing multi-modal and collaborative knowledge for representation denoising. Our unified architecture introduces two primary technical innovations. First, we integrate dimensionality reduction directly into the recommendation model, enabling end-to-end co-training that makes the reduction process aware of the final ranking objective. Second, we introduce a contrastive learning objective that explicitly incorporates the collaborative filtering signal into the latent space. This synergistic process refines raw LLM embeddings, filtering noise while amplifying task-relevant signals. Extensive experiments confirm our method's superior discriminative power, proving that this integrated fusion and denoising strategy is critical for achieving state-of-the-art performance. Our work provides a foundational paradigm for effectively harnessing LLMs in recommender systems.
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