论文标题
不确定性
Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
论文作者
论文摘要
大多数基于嵌入的建议模型都使用与低维空间中一个固定点相对应的嵌入式（向量）来表示用户和项目。这种嵌入无法精确地表示在推荐系统中经常观察到的不确定性的用户/项目。在解决这个问题时，我们提出了一个使用高斯嵌入的统一的深层建议框架，该框架被证明是一些用户表现出的不确定偏好的适应性，从而获得了更好的用户表示和建议性能。此外，我们的框架采用了蒙特卡洛抽样和卷积神经网络来计算客观用户与候选项目之间的相关性，这是基于确切的建议。我们在两个基准数据集上进行的广泛实验不仅证明我们提出的高斯嵌入方式很好地捕捉了用户的不确定性，而且还证明了其优于最先进的推荐模型的表现。
Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional space, to represent users and items. Such embeddings fail to precisely represent the users/items with uncertainty often observed in recommender systems. Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance. Furthermore, our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models.
