公开(公告)号：US11586941B2

公开(公告)日：2023-02-21

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

IPC: G06N5/04 ,  G06N20/00

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.

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

IPC: G06N5/02 ,  G06N5/04

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.

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

IPC: G06N5/04 ,  G06N20/00

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.