公开(公告)号：US20210216875A1

公开(公告)日：2021-07-15

申请号：US17218033

申请日：2021-03-30

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Tianjian He ,  Yi Liu ,  Daxiang Dong ,  Yanjun Ma ,  Dianhai Yu

IPC: G06N3/08 ,  G06N3/10

Abstract: A method for training a deep learning model may include: acquiring model description information and configuration information of a deep learning model; segmenting the model description information into at least two sections based on segmentation point variable in the configuration information, and loading the model description information to a corresponding resource to run; inputting a batch of training samples into a resource corresponding to a first section of model description information, then starting training and using obtained context information as an input of a resource corresponding to a subsequent section of model description information; and so on until an operation result of a resource corresponding to a final section of model description information is obtained; if a training completion condition is met, outputting a trained deep learning model; and otherwise, keeping on acquiring a subsequent batch of training samples and performing the above training steps until the condition is met.

公开(公告)号：US12292938B2

公开(公告)日：2025-05-06

申请号：US17399016

申请日：2021-08-10

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Tianjian He ,  Yi Liu ,  Daxiang Dong ,  Dianhai Yu ,  Yanjun Ma

IPC: G06F40/00 ,  G06F16/953 ,  G06F18/2323 ,  G06F18/2411 ,  G06F40/35

Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.