Patents by Inventor Xiuqiang He
Xiuqiang He has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240020541Abstract: This application describes a model training method, applied to the field of artificial intelligence. The method includes a computing core of a first processor obtains an embedding used for model training, and writes an updated embedding to a first memory of the first processor instead of transferring the updated embedding to a second processor after model training is completed. In this application, after updating an embedding, the first processor saves the updated embedding to the first memory of the first processor. Without needing to wait for the second processor to complete a process of transferring a second target embedding to a GPU, the first processor may directly obtain the updated embedding and perform model training of a next round based on the updated embedding, provided that the first processor may obtain a latest updated embedding.Type: ApplicationFiled: September 28, 2023Publication date: January 18, 2024Inventors: Wei GUO, Huifeng GUO, Yong GAO, Ruiming TANG, Wenzhi LIU, Xiuqiang HE
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Patent number: 11830033Abstract: 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.Type: GrantFiled: November 21, 2018Date of Patent: November 28, 2023Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Zhirong Liu, Ruiming Tang, Zhenhua Dong, Xiuqiang He, Guoxiang Cao
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Publication number: 20230306077Abstract: 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.Type: ApplicationFiled: June 1, 2023Publication date: September 28, 2023Inventors: Huifeng GUO, Bo CHEN, Ruiming TANG, Zhenguo LI, Xiuqiang HE
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Patent number: 11748452Abstract: 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.Type: GrantFiled: April 29, 2022Date of Patent: September 5, 2023Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Ruiming Tang, Huifeng Guo, Zhenguo Li, Xiuqiang He
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Patent number: 11734716Abstract: 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.Type: GrantFiled: November 21, 2018Date of Patent: August 22, 2023Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Zhirong Liu, Ruiming Tang, Zhenhua Dong, Xiuqiang He, Guoxiang Cao
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RECOMMENDATION MODEL TRAINING METHOD, RECOMMENDATION METHOD, APPARATUS, AND COMPUTER-READABLE MEDIUM
Publication number: 20230153857Abstract: A training method includes: obtaining a first recommendation model, where a model parameter of the first recommendation model is obtained through training based on n first training samples; determining an impact function value of each first training sample with respect to a verification loss of m second training samples in the first recommendation model; determining, based on the impact function value of each first training sample with respect to the verification loss, a weight corresponding to each first training sample; and training the first recommendation model based on the n first training samples and the weights corresponding to the n first training samples, to obtain a target recommendation model.Type: ApplicationFiled: January 19, 2023Publication date: May 18, 2023Inventors: Jingjie LI, Hong ZHU, Zhenhua DONG, Xiaolian ZHANG, Shi YIN, Xinhua FENG, Xiuqiang HE -
Publication number: 20230153579Abstract: 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.Type: ApplicationFiled: January 13, 2023Publication date: May 18, 2023Inventors: Jianing SUN, Yingxue ZHANG, Guo Huifeng, Ruiming TANG, Xiuqiang HE, Dengcheng ZHANG, Han YUAN
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Publication number: 20230129455Abstract: This application provides an application program (APP) management method, a terminal device, a server, and a system. According to the method, APPs downloaded on a terminal device can be automatically clustered. This saves time of a user and improves user experience. The method is applicable to a terminal device, and the method includes: obtaining a target desktop folder based on type information of an APP downloaded by the terminal device and attribute information of a desktop folder on the terminal device, where the downloaded APP is to be clustered in the target desktop folder; and clustering the downloaded APP into the target desktop folder.Type: ApplicationFiled: December 22, 2022Publication date: April 27, 2023Inventors: Bin WU, Xiuqiang HE, Li QIAN
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Publication number: 20230088171Abstract: A method and an apparatus for training a search recommendation model, and a method and an apparatus for sorting search results are provided. The training method includes: obtaining a training sample set including a sample user behavior group sequence and a masked sample user behavior group sequence; and using the training sample set as input data, and training a search recommendation model, to obtain a trained search recommendation model, where a target of the training is to obtain the object of the response operation of the sample user after the mask processing, the search recommendation model is used to predict a label of a candidate recommendation object in search results corresponding to a query field when a target user inputs the query field, and the label is used to indicate a probability that the target user performs a response operation on the candidate recommendation object.Type: ApplicationFiled: November 18, 2022Publication date: March 23, 2023Inventors: Guohao CAI, Gang WANG, Zhenhua DONG, Xiaoguang LI, Xiuqiang HE, Hong ZHU
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Patent number: 11580457Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.Type: GrantFiled: April 30, 2020Date of Patent: February 14, 2023Assignee: Huawei Technologies Co., Ltd.Inventors: Fei Chen, Zhenhua Dong, Zhenguo Li, Xiuqiang He, Li Qian, Shuaihua Peng
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Publication number: 20230031522Abstract: This application relates to the field of artificial intelligence.Type: ApplicationFiled: October 12, 2022Publication date: February 2, 2023Inventors: Bin Liu, Ruiming Tang, Huifeng Guo, Niannan Xue, Guilin Li, Xiuqiang He, Zhenguo Li
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Publication number: 20230026322Abstract: A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.Type: ApplicationFiled: September 20, 2022Publication date: January 26, 2023Inventors: Guilin Li, Bin Liu, Ruiming Tang, Xiuqiang He, Zhenguo Li
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Publication number: 20220261591Abstract: 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.Type: ApplicationFiled: April 29, 2022Publication date: August 18, 2022Inventors: Ruiming TANG, Huifeng GUO, Zhenguo LI, Xiuqiang HE
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Publication number: 20220198289Abstract: A recommendation model training method, a selection probability prediction method, and an apparatus are provided. The training method includes obtaining a training sample, where the training sample includes a sample user behavior log, position information of a sample recommended object, and a sample label. The training method further includes performing joint training on a position aware model and a recommendation model by the training sample, to obtain a trained recommendation model, where the position aware model predicts probabilities that a user pays attention to a target recommended object when the target recommended object is at different positions, and the recommendation model predicts, when the user pays attention to the target recommended object, a probability that the user selects the target recommended object.Type: ApplicationFiled: March 10, 2022Publication date: June 23, 2022Applicant: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Xiuqiang He
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Patent number: 11334758Abstract: 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.Type: GrantFiled: December 27, 2019Date of Patent: May 17, 2022Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Ruiming Tang, Huifeng Guo, Zhenguo Li, Xiuqiang He
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Patent number: 11281426Abstract: 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.Type: GrantFiled: June 27, 2019Date of Patent: March 22, 2022Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Bogdan Cautis, Ruiming Tang, Zhenhua Dong, Xiuqiang He, Zhirong Liu
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RECOMMENDATION MODEL TRAINING METHOD, RECOMMENDATION METHOD, APPARATUS, AND COMPUTER-READABLE MEDIUM
Publication number: 20210248651Abstract: This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.Type: ApplicationFiled: April 28, 2021Publication date: August 12, 2021Inventors: Chih Yao CHANG, Hong ZHU, Zhenhua DONG, Xiuqiang HE, Bowen YUAN -
Publication number: 20200258006Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.Type: ApplicationFiled: April 30, 2020Publication date: August 13, 2020Inventors: Fei CHEN, Zhenhua DONG, Zhenguo LI, Xiuqiang HE, Li QIAN, Shuaihua PENG
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Patent number: 10709934Abstract: A route planning method includes obtaining exercise capability information of a wearer and one or more candidate routes, where the candidate routes include attribute features that comprise historical exercise capability information, where the historical exercise capability information is information calculated according to a first preset rule and based on obtained exercise capability information of a plurality of users having exercised along the candidate routes; determining a target route based on the attribute features of the candidate routes and the exercise capability information of the wearer; and outputting the target route information.Type: GrantFiled: August 14, 2019Date of Patent: July 14, 2020Assignee: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Ruiming Tang, Xiuqiang He, Zhenhua Dong, Zhirong Liu, Yanjie Li
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Publication number: 20200134361Abstract: 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.Type: ApplicationFiled: December 27, 2019Publication date: April 30, 2020Inventors: Ruiming TANG, Huifeng GUO, Zhenguo LI, Xiuqiang HE