DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems

• URL: http://arxiv.org/abs/2512.00596v1
• Date: Sat, 29 Nov 2025 18:57:42 GMT
• Title: DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems
• Authors: Jiahao Tian, Zhenkai Wang,
• Abstract summary: Large Language Models (LLMs) generate rich, yet high-dimensional and noisy, multi-modal features.<n>Treating these features as static inputs decouples them from the core recommendation task.<n>We introduce a novel framework built on a key insight: deeply fusing multi-modal and collaborative knowledge for representation denoising.
• Score: 0.6875312133832079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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