Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22% in terms of recall@K on Top-K recommendation.

中文翻译：

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Top-K推荐的自适应余量概率度量学习

随着越来越多的内容和服务可用以及用户难以确定可能感兴趣的推荐系统，个性化推荐系统发挥着越来越重要的作用。尽管已证明矩阵分解和基于深度学习的方法在用户首选项建模中是有效的，但它们违反了三角不等式并且无法捕获细粒度的首选项信息。为了解决这个问题，我们开发了一种基于距离的推荐模型，该模型具有几个新颖的方面：（i）每个用户和商品都通过高斯分布进行参数化，以捕获学习的不确定性；（ii）提出了一种自适应余量生成方案，以生成有关不同训练三胞胎的余量；（iii）在目标函数中加入了明确的用户-用户/项目-项目相似性建模。Wasserstein距离用于确定偏好，因为它服从三角形不等式并且可以测量概率分布之间的距离。通过使用五个最真实的数据集与最新方法进行比较，在Top-K推荐的召回率K方面，所提出的模型比现有的最佳模型高出4-22％。