Abstract
Modern recommender systems follow the guiding principle of serving the right user, the right item at the right time. One of their main limitations is that they are typically limited to items already in the catalog. We propose REcommendations BEyond CAtalogs, REBECA, a new class of probabilistic diffusion-based recommender systems that synthesize new items tailored to individual tastes rather than retrieve items from the catalog. REBECA combines efficient training in embedding space with a novel diffusion prior that only requires users' past ratings of items. We evaluate REBECA on real-world data and propose novel personalization metrics for generative recommender systems. Extensive experiments demonstrate that REBECA produces high-quality, personalized recommendations, generating images that align with users' unique preferences.
Abstract (translated)
现代推荐系统遵循的原则是在正确的时间向正确的用户提供合适的物品。然而，它们的主要局限性在于通常只能从现有的商品目录中提供项目。我们提出了一个名为REBECA（REcommendations BEyond CAtalogs）的新类概率扩散型推荐系统，该系统可以合成新的个性化内容以满足用户的独特偏好，而不仅仅是从现有目录中检索物品。REBECA结合了嵌入空间中的高效训练与一种新颖的仅依赖于用户过去对商品评分的概率扩散先验方法。 我们在真实世界的数据上评估了REBECA，并提出了用于生成式推荐系统的新型个性化指标。广泛的实验表明，REBECA能够产生高质量且个性化的推荐结果，生成的图像符合用户的独特偏好。
URL
https://arxiv.org/abs/2502.18477
