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公开(公告)号：US11599594B2
公开(公告)日：2023-03-07
申请号：US17829113
申请日：2022-05-31
Inventor： Yaqing Wang , Dejing Dou
IPC: G06F16/955 , G06F16/9532 , G06F16/906 , G06F16/903 , G06F16/9538
Abstract: A method for data processing is provided. The method includes obtaining first retrieving data associated with a first user and a first retrieving result selected by the first user from at least one retrieving result corresponding to the first retrieving data. The first retrieving data is labelled with an intention tag indicating a retrieving intention of the first user. The method further includes obtaining second retrieving data that is used by a second user to conduct retrieving and selecting the first retrieving result within a predetermined time period. The method further includes assigning the intention tag to the second retrieving data.
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公开(公告)号：US20240104403A1
公开(公告)日：2024-03-28
申请号：US18521061
申请日：2023-11-28
Inventor： Yaqing Wang , Hongming Piao , Longteng Xu , Daxiang Dong , Jingbo Zhou
IPC: G06N5/022
CPC classification number: G06N5/022
Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.
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公开(公告)号：US20220237376A1
公开(公告)日：2022-07-28
申请号：US17718285
申请日：2022-04-11
Inventor： Yaqing Wang , Dejing Dou
IPC: G06F40/279 , G06F40/253 , G06F40/117 , G06N3/04
Abstract: A computer-implemented method for text classification is provided. The method for text classification includes obtaining an entity category set and a part-of-speech tag set associated with a text. The method further includes constructing a first isomorphic graph for the entity category set and a second isomorphic graph for the part-of-speech tag set. A node of the first isomorphic graph corresponds to an entity category in the entity category set, and a node of the second isomorphic graph corresponds to a part-of-speech tag in the part-of-speech tag set. The method further includes obtaining, based on the first isomorphic graph and the second isomorphic graph, a first text feature and a second text feature of the text through a graph neural network. The method further includes classifying the text based on a fused feature of the first text feature and the second text feature.
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公开(公告)号：US20210312288A1
公开(公告)日：2021-10-07
申请号：US17349280
申请日：2021-06-16
Inventor： Yaqing Wang , Dejing Dou
Abstract: The present application discloses a method for training a classification model, a classification method, an apparatus and a device. A specific implementation is: acquiring behavior information of multiple users and personal basic information of the multiple users; where categories of at least part of users of the multiple users are known; inputting the personal basic information of the multiple users into a classification model to be trained to obtain feature information of the multiple users and predicted categories of users with known categories; and training the classification model to be trained according to the behavior information of the multiple users, the feature information of the multiple users, the predicted categories of the users with the known categories, and real categories of the users with the known categories, to obtain a trained classification model. The user categories determined by using the classification model are more accurate.
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