公开(公告)号：US20190317729A1

公开(公告)日：2019-10-17

申请号：US16455152

申请日：2019-06-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Bogdan CAUTIS ,  Ruiming TANG ,  Zhenhua DONG ,  Xiuqiang HE ,  Zhirong LIU

IPC: G06F7/24 ,  G06F17/18 ,  G06F3/0482

Abstract: An application sorting method and apparatus are provided. The method includes: obtaining, a positive operation probability and positive operation feedback information of each of at least two data samples; calculating an uncertainty parameter of a positive operation probability of a first data sample based on the positive operation probabilities and the positive operation feedback information of the at least two data samples and feature indication information of at least one same feature in a plurality of features in the at least two data samples; and correcting the positive operation probability of the first data sample by using the uncertainty parameter of the positive operation probability; and sorting, based on corrected positive operation probabilities, application programs corresponding to the at least two data samples.

公开(公告)号：US20240242127A1

公开(公告)日：2024-07-18

申请号：US18620051

申请日：2024-03-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yichao WANG ,  Bo CHEN ,  Ruiming TANG ,  Xiuqiang HE ,  Hongkun ZHENG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: This application discloses an information recommendation method, which may be applied to the field of artificial intelligence. The method includes: obtaining a target feature vector; and processing the target feature vector by using a recommendation model, to obtain recommendation information, where the recommendation model includes a cross network, a deep network, and a target network; the target network is used to perform fusion processing on a first intermediate output that is output by the first cross layer and a second intermediate output that is output by the first deep layer, to obtain a first fusion result, and the target network is further used to: process the first fusion result to obtain a first weight corresponding to the first cross layer and a second weight corresponding to the first deep layer, and weight the first fusion result with the first weight and the second weight separately.

公开(公告)号：US20230306077A1

公开(公告)日：2023-09-28

申请号：US18327584

申请日：2023-06-01

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Huifeng GUO ,  Bo CHEN ,  Ruiming TANG ,  Zhenguo LI ,  Xiuqiang HE

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: Embodiments of this application provide a data processing method and apparatus to better learn a vector representation value of each feature value in a continuous feature. The method specifically includes: The data processing apparatus obtains the continuous feature from sample data, and then performs discretization processing on the continuous feature by using a discretization model, to obtain N discretization probabilities corresponding to the continuous feature. The N discretization probabilities correspond to N preset meta-embeddings, and N is an integer greater than 1. Finally, the data processing apparatus determines a vector representation value of the continuous feature based on the N discretization probabilities and the N meta-embeddings.

公开(公告)号：US20230153579A1

公开(公告)日：2023-05-18

申请号：US18154523

申请日：2023-01-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jianing SUN ,  Yingxue ZHANG ,  Guo Huifeng ,  Ruiming TANG ,  Xiuqiang HE ,  Dengcheng ZHANG ,  Han YUAN

IPC: G06N3/0464 ,  G06N3/08

CPC classification number: G06N3/0464 ,  G06N3/08

Abstract: Method and system for processing a bipartite graph that comprises a plurality of first nodes of a first node type, and a plurality of second nodes of a second type, comprising: generating a target first node embedding for a target first node based on features of second nodes and first nodes that are within a multi-hop first node neighbourhood of the target first node, the target first node being selected from the plurality of first nodes of the first node type; generating a target second node embedding for a target second node based on features of first nodes and second nodes that are within a multi-hop second node neighbourhood of the target second node, the target second node being selected from the plurality of second nodes of the second node type; and determining a relationship between the target first node and the target second node based on the target first node embedding and the target second node embedding.

公开(公告)号：US20220261591A1

公开(公告)日：2022-08-18

申请号：US17661448

申请日：2022-04-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Ruiming TANG ,  Huifeng GUO ,  Zhenguo LI ,  Xiuqiang HE

IPC: G06K9/62

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m≥3, and m>n≥2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

公开(公告)号：US20190087884A1

公开(公告)日：2019-03-21

申请号：US16198704

申请日：2018-11-21

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhirong LIU ,  Ruiming TANG ,  Zhenhua DONG ,  Xiuqiang HE ,  Guoxiang CAO

IPC: G06Q30/06 ,  G06F17/30

Abstract: The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.