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公开(公告)号：US20210326729A1
公开(公告)日：2021-10-21
申请号：US17360581
申请日：2021-06-28
Applicant: Huawei Technologies Co., Ltd.
Inventor： Hong Zhu , Zhenhua Dong , Ruiming Tang , Yuzhou Zhang , Li Qian
Abstract: A recommendation model training method includes selecting a positive sample in a sample set, and adding the positive sample to a training set, where the sample set includes the positive sample and negative samples, each sample includes n sample features, n≥1, and the sample features of each sample include a feature used to represent whether the sample is a positive sample or a negative sample, calculating sampling probabilities of the negative samples in the sample set by using a preset algorithm, selecting a negative sample from the sample set based on the sampling probability, and adding the negative sample to the training set, and performing training by using the samples in the training set, to obtain a recommendation model.
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公开(公告)号：US20230026322A1
公开(公告)日：2023-01-26
申请号：US17948392
申请日：2022-09-20
Applicant: Huawei Technologies Co., Ltd.
Inventor： Guilin Li , Bin Liu , Ruiming Tang , Xiuqiang He , Zhenguo Li
Abstract: 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.
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13.发明申请公开(公告)号：US20220198289A1
公开(公告)日：2022-06-23
申请号：US17691843
申请日：2022-03-10
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Huifeng Guo , Jinkai Yu , Qing Liu , Ruiming Tang , Xiuqiang He
Abstract: 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.
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公开(公告)号：US20200272913A1
公开(公告)日：2020-08-27
申请号：US15931224
申请日：2020-05-13
Applicant: Huawei Technologies Co., Ltd.
Inventor： Jinkai Yu , Ruiming Tang , Zhenhua Dong , Yuzhou Zhang , Weiwen Liu , Li Qian
Abstract: A recommendation method includes generating a feature sequence based on to-be-predicted data of a user for a target object and according to a preset encoding rule, obtaining probability distribution information corresponding to each feature in the feature sequence, and obtaining, through calculation, a feature vector corresponding to each feature, obtaining a predicted score of the user for the target object based on values of N features and a feature vector corresponding to each of the N features, and recommending the target object to the user when the predicted score is greater than or equal to a preset threshold.
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Patent Agency Ranking
15.发明申请公开(公告)号：US20190043076A1
公开(公告)日：2019-02-07
申请号：US15865273
申请日：2018-01-09
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Zhenhua Dong , Zhirong Liu , Xiuqiang He , Ruiming Tang , Bohai Yang
Abstract: An advertisement management server in a communication system receives information of advertisements, determines an advertising value calculation policy, estimates a value of an advertising value element according to the information of each advertisement, calculates a value of each advertisement using the value of the advertising value element and the advertising value calculation policy as reference factors. The server instructs the communication system to broadcast the advertisements for displaying on the user terminals according to the calculated value of each advertisement.
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公开(公告)号：US12182141B2
公开(公告)日：2024-12-31
申请号：US18191704
申请日：2023-03-28
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Weiwen Liu , Yunjia Xi , Jianghao Lin , Ruiming Tang , Weinan Zhang , Yong Yu
IPC: G06F16/2457 , G06F16/248 , G06F40/103
Abstract: A system is provided for reranking. The system comprises a user device and one or more servers. The system is configured to receive a plurality of candidate lists, rerank the plurality of candidate lists based on page-level information and a format of a recommendation page, generate recommendation results based on the reranked lists, and send the recommendation results to the user device. Each candidate list comprises a plurality of candidate items. The page-level information comprises interactions between the candidate items in each candidate list and between different candidate lists among the plurality of candidate lists. The reranking comprises using the format of the recommendation page to determine pairwise item influences between candidate item pairs among the candidate items in the candidate lists. The user device is configured to display the recommendation page with the recommendation results from the one or more servers.
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公开(公告)号：US20240202491A1
公开(公告)日：2024-06-20
申请号：US18416924
申请日：2024-01-19
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Wei Guo , Jiarui Qin , Ruiming Tang , Zhirong Liu , Xiuqiang He , Weinan Zhang , Yong Yu
IPC: G06N3/04 , G06Q30/0601
CPC classification number: G06N3/04 , G06Q30/0631
Abstract: A recommendation device obtains to-be-predicted data and a plurality of target reference samples based on a similarity between the to-be-predicted data and the plurality of reference samples. Each reference sample and the to-be-predicted data each include user feature field data indicating a feature of a target user, and item feature field data indicating a feature of a target item. Each target reference sample and the to-be-predicted data have partially identical user feature field data and/or item feature field data. The recommendation device obtains target feature information of the to-be-predicted data based on the plurality of target reference samples and the to-be-predicted data. The recommendation device then uses the target feature information as input to a deep neural network to obtain a target item that is to be recommended.
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公开(公告)号：US11748452B2
公开(公告)日：2023-09-05
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: G06F18/2321 , G06F18/2451 , G06F18/2133 , G06F18/2453
CPC classification number: G06F18/2321 , G06F18/2133 , G06F18/2451 , G06F18/2453
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.
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公开(公告)号：US20230031522A1
公开(公告)日：2023-02-02
申请号：US17964117
申请日：2022-10-12
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Bin Liu , Ruiming Tang , Huifeng Guo , Niannan Xue , Guilin Li , Xiuqiang He , Zhenguo Li
Abstract: This application relates to the field of artificial intelligence. A recommendation method based on automatic feature grouping includes: obtaining a plurality of candidate recommended objects and a plurality of association features of each of the plurality of candidate recommended objects; performing multi-order automatic feature grouping on the plurality of association features of each candidate recommended object, to obtain a multi-order feature interaction set of each candidate recommended object; obtaining an interaction feature contribution value of each candidate recommended object through calculation based on the plurality of association features in the multi-order feature interaction set of each candidate recommended object; obtaining a prediction score of each candidate recommended object through calculation based on the interaction feature contribution value of each candidate recommended object; and determining one or more corresponding candidate recommended objects with a high prediction score as a target recommended object.
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