公开(公告)号：US20160371589A1

公开(公告)日：2016-12-22

申请号：US14748333

申请日：2015-06-24

Applicant: Yahoo! Inc.

Inventor： Nadav GOLBANDI ,  Chao Wang

IPC: G06N5/04 ,  G06N7/00 ,  G06N99/00

CPC classification number: G06N20/00 ,  G06F16/9535 ,  G06Q30/0241 ,  G06Q30/0269

Abstract: The present disclosure relates to computer systems implementing methods for online content recommendation. The computer systems may be configured to receive a training sample from a first client device corresponding to a predefined feedback interacting with online content displayed on the first client device; update a preexisting training database in real-time based on the received training sample to generate an updated training sample, wherein prior to being updated based on the training sample received from the first client, the training database includes a set of historical training samples; conduct a regression training to a computer learning model in real-time, using the updated training sample, to produce a set of trained parameters for an online content recommendation model; call the set of trained parameters in real-time to determine recommend online content for a second user with the online content recommendation model; and send the recommended online content to a second client device of the second user.

Abstract translation: 本公开涉及实现在线内容推荐方法的计算机系统。 计算机系统可以被配置为从第一客户端设备接收对应于与在第一客户端设备上显示的在线内容交互的预定义反馈的训练样本; 基于所接收的训练样本来实时更新预先存在的训练数据库以生成更新的训练样本，其中在根据从第一客户端接收到的训练样本进行更新之前，训练数据库包括一组历史训练样本; 对计算机学习模型实时进行回归训练，使用更新的训练样本，为在线内容推荐模型生成一组经过训练的参数; 通过在线内容推荐模型实时调用一组受过训练的参数，以确定第二个用户的推荐在线内容; 并将推荐的在线内容发送给第二个用户的第二个客户端设备。