用扩散模型生成用户专属商品包，解决冷启动推荐难题。
Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation
- 基于个性化扩散模型生成符合用户兴趣的物品组合
- 在三个真实数据集上显著优于五种基线方法
- 适合研究冷启动推荐与生成式推荐的学者
捆绑推荐旨在为每位用户推荐一组物品。然而，用户与捆绑项之间的交互稀疏性带来了巨大挑战，尤其在冷启动场景下。传统协同过滤方法在此类问题上表现不佳，因其依赖交互信息更新潜在嵌入，在冷启动情况下难以奏效。我们提出一种新方法（DisCo），采用个性化扩散主干网络，并通过解耦用户兴趣的多个方面，在分布空间中为每个用户生成符合分布的捆绑项。训练阶段，DisCo引入额外的目标损失项以避免偏差，这是使用生成模型进行Top-K推荐时的常见问题。实验结果表明，DisCo在三个真实数据集上显著优于五种对比基线。本研究构建了一个有前景的框架，并提供了冷启动推荐的重要视角。可复现材料见：https://github.com/bt-nghia/DisCo。
原文摘要 · Abstract (English)
Bundle recommendation aims to recommend a set of items to each user. However, the sparser interactions between users and bundles raise a big challenge, especially in cold-start scenarios. Traditional collaborative filtering methods do not work well for this kind of problem because these models rely on interactions to update the latent embedding, which is hard to work in a cold-start setting. We propose a new approach (DisCo), which relies on a personalized Diffusion backbone, enhanced by disentangled aspects for the user's interest, to generate a bundle in distribution space for each user to tackle the cold-start challenge. During the training phase, DisCo adjusts an additional objective loss term to avoid bias, a prevalent issue while using the generative model for top-$K$ recommendation purposes. Our empirical experiments show that DisCo outperforms five comparative baselines by a large margin on three real-world datasets. Thereby, this study devises a promising framework and essential viewpoints in cold-start recommendation. Our materials for reproducibility are available at: https://github.com/bt-nghia/DisCo.
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