公开(公告)号：US20210374174A1

公开(公告)日：2021-12-02

申请号：US17128047

申请日：2020-12-19

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

Inventor： Chaoxing CHEN ,  Zhengjie HUANG ,  Shikun FENG

IPC: G06F16/438 ,  G06F16/901 ,  G06F17/18 ,  G06F17/16

Abstract: Embodiments of the disclosure disclose a method and apparatus for recommending multimedia resources, an electronic device and a storage medium. The present disclosure relates to a nature language processing technology. The technology solution can be implemented by generating a relation graph based on first multimedia resources browsed by a plurality of user objects, taking two nodes between which a number of nodes in a path of the relation graph is less than a number threshold as a training sample, training a graph model to obtain a representative vector of each node in the training samples and recommending multimedia resources based on vector similarities between the representative vectors of respective nodes of the plurality of user objects and/or the representative vectors of respective nodes of the first multimedia resources.

公开(公告)号：US20210390393A1

公开(公告)日：2021-12-16

申请号：US17128978

申请日：2020-12-21

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

Inventor： Zhengjie HUANG ,  Weibin LI ,  Shikun FENG

IPC: G06N3/08 ,  G06K9/62

Abstract: A method for pre-training a graph neural network, an electronic device and a readable storage medium, which relate to the technical field of deep learning are proposed. An embodiment for pre-training a graph neural network includes: acquiring an original sample to be used for training; expanding the original sample to obtain a positive sample and a negative sample corresponding to the \original sample; constructing a sample set Corresponding to the original sample by using the original sample and the positive sample, the negative sample, and a weak sample corresponding to the original sample; and pre-training the graph neural network by taking the original sample and one of other samples in the sample set as input of the graph neural network respectively, until the graph neural network converges. The technical solution may implement pre-training of a graph neural network at a graph level.

公开(公告)号：US20210200806A1

公开(公告)日：2021-07-01

申请号：US16895996

申请日：2020-06-08

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

Inventor： Zhengjie HUANG ,  Chaoxing CHEN ,  Shikun FENG ,  Hua LU

IPC: G06F16/901 ,  G06F16/903 ,  G06N3/08 ,  G06F9/38

Abstract: Embodiments of the present disclosure relate to a method and apparatus for parallel processing of information. The method may include: detecting whether a source node in a graph structure processed by a graph neural network sends information to a target node; generating and recording, in response to detecting that the source node sends the information to the target node, a target information sequence and an index for the target information sequence, target information being determined based on a characteristic of the source node, a characteristic of the target node, and the sent information, and the index being used to group the target information sequence; using the index to group the target information sequence; and processing groups of target information in parallel.