论文标题
规范张量缩放
Canonical Tensor Scaling
论文作者
论文摘要
在本文中，我们将矩阵的行和列的典型尺度概括为任意张量的选定rank子镜的缩放。我们希望我们的结果和框架对推荐系统问题的概括所需的稀疏张量完成将被证明是有用的，除了对多维阵列的用户产品评级矩阵矩阵，涉及基于用户属性的坐标（例如，年龄，性别，地理位置，地理位置等）和产品/产品/项目/物品属性（例如，价格，价格，尺寸，尺寸，重量等）。
In this paper we generalize the canonical positive scaling of rows and columns of a matrix to the scaling of selected-rank subtensors of an arbitrary tensor. We expect our results and framework will prove useful for sparse-tensor completion required for generalizations of the recommender system problem beyond a matrix of user-product ratings to multidimensional arrays involving coordinates based both on user attributes (e.g., age, gender, geographical location, etc.) and product/item attributes (e.g., price, size, weight, etc.).
