将无分类器引导引入扩散推荐模型，提升稀疏数据下的推荐效果。
Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation
- 用扩散模型模拟用户浏览与评分序列，结合无分类器引导增强生成能力。
- 在多个数据集上优于现有推荐系统，尤其在数据稀疏场景下表现更优。
- 适合处理冷启动和数据稀疏的推荐任务，可为新用户/物品提供更好建议。
本文提出一种融合无分类器引导的扩散推荐模型。当前多数推荐系统依赖协同过滤或内容过滤等传统方法。扩散模型作为生成式AI的新范式，相较变分自编码器（VAEs）和生成对抗网络（GANs）有更好表现。本工作将扩散模型用于推荐系统，模拟用户浏览与评分物品的序列行为。尽管已有少数推荐系统采用扩散模型，但未整合无分类器引导这一扩散模型的重要创新。本文提出的模型不仅增强底层推荐结构，还引入无分类器引导机制。实验表明，在多个数据集上的多种推荐任务中，该方法在多数指标上超越现有最优模型。特别地，其在数据稀疏场景下展现出显著优势，具备改善冷启动推荐的潜力。
原文摘要 · Abstract (English)
This paper presents a diffusion-based recommender system that incorporates classifier-free guidance. Most current recommender systems provide recommendations using conventional methods such as collaborative or content-based filtering. Diffusion is a new approach to generative AI that improves on previous generative AI approaches such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items. Although a few current recommender systems incorporate diffusion, they do not incorporate classifier-free guidance, a new innovation in diffusion models as a whole. In this paper, we present a diffusion recommender system that augments the underlying recommender system model for improved performance and also incorporates classifier-free guidance. Our findings show improvements over state-of-the-art recommender systems for most metrics for several recommendation tasks on a variety of datasets. In particular, our approach demonstrates the potential to provide better recommendations when data is sparse.
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