公开(公告)号：US20120096009A1

公开(公告)日：2012-04-19

申请号：US12906551

申请日：2010-10-18

申请人： Martin B. Scholz ,  Shyamsundar Rajaram ,  Rajant Lukose

发明人： Martin B. Scholz ,  Shyamsundar Rajaram ,  Rajant Lukose

IPC分类号： G06F17/30

CPC分类号： G06F17/30699

摘要： Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in a binary ratings matrix having a particular dimensional space. Unknown values in the binary ratings matrix are weighted with a weight matrix having the particular dimensional space. The binary ratings matrix and the weight matrix are hashed into a lower dimensional space by one of row and column. The hashed binary ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the binary ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.

摘要翻译： 提供系统，方法和机器可读和可执行指令用于协同过滤。 协同过滤包括用具有特定尺寸空间的二进制评级矩阵中的行和列来表示用户和对象。 二进制等级矩阵中的未知值用具有特定尺寸空间的权重矩阵加权。 二进制等级矩阵和权重矩阵通过行和列之一被散列成较低维的空间。 散列二进制等级矩阵和散列权重矩阵是通过交替的最小二乘法近似的低阶。 使用二进制等级矩阵和权重矩阵来更新行和列之一的低阶近似的结果。 可以基于更新的结果为一个用户生成其中一个对象的推荐。