公开(公告)号：US20210326730A1

公开(公告)日：2021-10-21

申请号：US17363101

申请日：2021-06-30

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

Inventor： Yaqing WANG ,  Dejing DOU

IPC: G06N5/04 ,  G06F40/174 ,  G06N20/00

Abstract: A method for information completion, an electronic device and a storage medium, related to the fields of artificial intelligence, big data, deep learning and the like, are provided. The method includes: acquiring an actual information form and an initialization information form, wherein the actual information form includes an information form which is filled out by a plurality of users and in which target information is missing, and the initialization information form is an information form in which there is target information at each target information position; performing an adjustment on the initialization information form by utilizing a similarity between the users, a low-rank constraint of the initialization information form and a difference between the initialization information form and the actual information form, to obtain an adjusted information form; and supplementing target information in the adjusted information form to a position, in the actual information form.

公开(公告)号：US20210390394A1

公开(公告)日：2021-12-16

申请号：US17171507

申请日：2021-02-09

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

Inventor： Yaqing WANG ,  Hui XIONG

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: The present disclosure provides a method for generating a recommendation model, a content recommendation method, and a content recommendation apparatus, and an electronic device, and relates to an artificial intelligence field and a deep learning field. The method for generating a recommendation model includes: obtaining a graph training sample set; inputting the graph training sample set into a machine learning model to train the machine learning model, in which the machine learning model includes at least one low-rank graph convolutional network, and the low-rank graph convolutional network includes a complete weight matrix composed of a first low-rank matrix and a second low-rank matrix; in which a training objective of the low-rank graph convolutional network includes a first parameter item, a second parameter item and a non-convex low-rank item; and in responding to detecting that a training end condition is met, determining the machine learning model as a recommendation model.