无需外部知识，通过节点级生成增强推荐系统性能。
Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
- 提出NodeDiffRec框架，基于扩散模型生成伪物品与交互
- 在无外部知识下实现98.6%的Recall@5提升，达到SOTA效果
- 适合资源受限场景，提升推荐多样性与结构连通性
当前推荐系统依赖知识图谱或大语言模型等外部资源，限制了实际应用。尽管无知识模型可通过直接边操作增强推荐，但缺乏增广原语导致难以弥合语义与结构鸿沟。本文提出NodeDiffRec，首个无知识增广框架，通过扩散模型实现细粒度节点级图生成，合成符合潜在分布的伪物品及其交互，并借助去噪偏好建模优化用户偏好。实验表明，该方法在多个数据集和算法上表现优异，相比基线最大提升98.6%（Recall@5）和84.0%（NDCG@5），显著增强语义多样性与结构连通性，无需外部知识即可实现领先性能。
原文摘要 · Abstract (English)
Recent advances in recommender systems rely on external resources such as knowledge graphs or large language models to enhance recommendations, which limit applicability in real-world settings due to data dependency and computational overhead. Although knowledge-free models are able to bolster recommendations by direct edge operations as well, the absence of augmentation primitives drives them to fall short in bridging semantic and structural gaps as high-quality paradigm substitutes. Unlike existing diffusion-based works that remodel user-item interactions, this work proposes NodeDiffRec, a pioneering knowledge-free augmentation framework that enables fine-grained node-level graph generation for recommendations and expands the scope of restricted augmentation primitives via diffusion. By synthesizing pseudo-items and corresponding interactions that align with the underlying distribution for injection, and further refining user preferences through a denoising preference modeling process, NodeDiffRec dramatically enhances both semantic diversity and structural connectivity without external knowledge. Extensive experiments across diverse datasets and recommendation algorithms demonstrate the superiority of NodeDiffRec, achieving State-of-the-Art (SOTA) performance, with maximum average performance improvement 98.6% in Recall@5 and 84.0% in NDCG@5 over selected baselines.
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