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1. 发明申请

US20190294588A1 TEXT DEDUPLICATION METHOD AND APPARATUS, AND STORAGE MEDIUM 审中-公开
公开(公告)号：US20190294588A1
公开(公告)日：2019-09-26
申请号：US16441669
申请日：2019-06-14
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Wei XU , Li ZHONG , Li WANG , Lichun LIU
IPC分类号： G06F16/174 , G06F16/31 , G06F17/27
摘要： A text deduplication method and apparatus, and a storage medium are provided. The method includes: obtaining a text set, the text set including a plurality of to-be-deduplicated texts; capturing, for each to-be-deduplicated text, a corresponding subtext string from the to-be-deduplicated text; and determining, in the text set, to-be-deduplicated texts having a same subtext string, to obtain text subsets. Each subtext string corresponds to a text subset, and each text subset includes one or more to-be-deduplicated texts that have the corresponding subtext string. The method also includes performing text deduplication processing on the text subset corresponding to each subtext string, to obtain a deduplicated text set corresponding to each subtext string; and obtaining, according to the deduplicated text set corresponding to each subtext string, a result text set of the text set after the deduplication.

2. 发明申请

US20230031156A1 MACHINE LEARNING MODEL TRAINING METHOD AND APPARATUS, SERVER, AND STORAGE MEDIUM 有权
公开(公告)号：US20230031156A1
公开(公告)日：2023-02-02
申请号：US17959858
申请日：2022-10-04
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Wei ZHAO , Yabing FENG , Yu LIAO , Junbin LAI , Haixia CHAI , Xuanliang PAN , Lichun LIU
IPC分类号： G06K9/62 , G06N20/00
摘要： A machine learning model training method includes: training a machine learning model using features of samples in a training set, where a sample in the training set corresponds to an initial first weight and an initial second weight. In one iteration, the method includes: determining a first sample set comprising one or more samples whose corresponding target variables are incorrectly predicted; determining an overall predicted loss of the first sample set based on the predicted losses and corresponding first weights of samples in the first sample set; updating the first weights and second weights of the samples in the first sample set based on the overall predicted loss of the first sample set; and inputting the second weights, the features, and the target variables of the samples in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.

3. 发明申请

US20170109431A1 METHOD AND APPARATUS FOR GROUPING NETWORK SERVICE USERS 有权
公开(公告)号：US20170109431A1
公开(公告)日：2017-04-20
申请号：US15395714
申请日：2016-12-30
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Xiangyong YANG , Zhibing Al , Lichun LIU , Chuan CHEN
IPC分类号： G06F17/30 , H04L29/08
CPC分类号： G06F17/30598 , G06F17/30569 , H04L12/1859 , H04L51/38 , H04L67/26
摘要： A method for grouping network service users includes acquiring attribute and/or behavior data of multiple users within a current period, and converting the attribute and/or behavior data into standardized data; determining multiple group central points according to the standardized data, and placing the standardized data in a group where a group central point having a shortest distance is located; determining group features of groups according to standardized data in the groups; and separately pushing corresponding service push information to users in the groups according to the group features of the groups. In addition, an apparatus for grouping network service users is further described.

4. 发明申请

US20160217491A1 DEVICES AND METHODS FOR PREVENTING USER CHURN 审中-公开
标题翻译：用于防止用户启动的设备和方法
公开(公告)号：US20160217491A1
公开(公告)日：2016-07-28
申请号：US15089255
申请日：2016-04-01
申请人： Tencent Technology (Shenzhen) Company Limited
发明人： Jingtao ZHU , Xi HU , Xin XU , Xiaolong ZHANG , Hu NI , Duobin XU , Lichun LIU , Chengtao FAN , Zhibing AI , Xiangyong YANG
IPC分类号： G06Q30/02 , G06F17/30 , H04L29/08
CPC分类号： G06Q30/0255 , G06F11/3438 , G06F11/3476 , G06F16/285 , G06F2201/81 , G06F2201/865 , H04L67/22
摘要： Devices and methods are provided for preventing user churn, wherein the methods include: collecting target user data corresponding to one or more target users associated with a target application program (101), the target user data including user basic attribute information, user behavioral indicator information and user active indicator information; determining a target user type of the one or more target users based on at least information associated with the target user data of the one or more target users (102), the target user type including a normal active user, an approximately silent user and a silent user; and in response to the target user type of the one or more target users being an approximately silent user, pushing first data for promoting activeness to the one or more target users associated with the target application program (103).
摘要翻译：提供了用于防止用户流失的装置和方法，其中所述方法包括：收集与目标应用程序（101）相关联的一个或多个目标用户对应的目标用户数据，所述目标用户数据包括用户基本属性信息，用户行为指示符信息和用户活动指示符信息; 至少基于与所述一个或多个目标用户（102）的目标用户数据相关联的信息来确定所述一个或多个目标用户的目标用户类型，所述目标用户类型包括正常活动用户，近乎无声的用户和沉默的用户; 并且响应于所述一个或多个目标用户的目标用户类型是近乎无声的用户，推动第一数据以促进与所述目标应用程序（103）相关联的所述一个或多个目标用户的活动。

5. 发明申请

US20160004699A1 METHOD AND DEVICE FOR RECOMMENDATION OF MEDIA CONTENT 审中-公开
标题翻译：用于推荐媒体内容的方法和装置
公开(公告)号：US20160004699A1
公开(公告)日：2016-01-07
申请号：US14589086
申请日：2015-01-05
申请人： Tencent Technology (Shenzhen) Company Limited
发明人： Lichun LIU , Jingtao ZHU , Xiang Wang , Chengtao Fan , Bin Zhou , Shenyuan Li , Jianfeng Chen , Siliang Huang , Juan Sun , Huguang Jin , Dan Li , Chunxia Qin
IPC分类号： G06F17/30
CPC分类号： G06F16/24578 , G06F16/435 , G06F16/9535 , H04N21/251
摘要： A method and a device for recommendation of media content are described. The method includes: calculating scores of media content labels in a computer media content library; in descending order of the scores, selecting a first threshold media content label as a candidate media content label; for the candidate media content label, finding out a media content corresponding to the candidate media content label from the media content library; for the media content corresponding to the candidate media content label, in descending order of the amount of page views, selecting a second threshold media content as a media content to be recommended corresponding to the candidate media content label; and recommending the media content to be recommended corresponding to the candidate media content label to a user. The method and device can recommend popular media contents to users, and reduce the style difference between the media contents.
摘要翻译：描述了媒体内容推荐的方法和装置。该方法包括：在计算机媒体内容库中计算媒体内容标签的分数; 以分数的降序，选择第一阈值媒体内容标签作为候选媒体内容标签; 对于所述候选媒体内容标签，从所述媒体内容库中找出与所述候选媒体内容标签对应的媒体内容; 对于与候选媒体内容标签相对应的媒体内容，按照页面浏览量的降序，选择第二阈值媒体内容作为与候选媒体内容标签相对应的推荐媒体内容; 并向使用者推荐与候选媒体内容标签对应的媒体内容。该方法和设备可以向用户推荐流行的媒体内容，并减少媒体内容之间的风格差异。

6. 发明申请

US20190318202A1 MACHINE LEARNING MODEL TRAINING METHOD AND APPARATUS, SERVER, AND STORAGE MEDIUM 审中-公开
公开(公告)号：US20190318202A1
公开(公告)日：2019-10-17
申请号：US16383140
申请日：2019-04-12
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Wei ZHAO , Yabing FENG , Yu LIAO , Junbin LAI , Haixia CHAI , Xuanliang PAN , Lichun LIU
IPC分类号： G06K9/62 , G06N20/00
摘要： A machine learning model training method includes: training a machine learning model using features of each sample in a training set based on an initial first weight and an initial second weight. In one iteration, the method includes determining a first sample set in which a target variable is incorrectly predicted, and a second sample set in which a target variable is correctly predicted, based on a predicted loss of each sample; and determining overall predicted loss of the first sample set based on a predicted loss and a first weight of each sample in the first sample set. The method also includes updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss; and inputting the updated second weight, the features, and the target variable of each sample to the machine learning model, and initiating a next iteration.

7. 发明申请

US20190236412A1 DATA PROCESSING METHOD AND DEVICE, CLASSIFIER TRAINING METHOD AND SYSTEM, AND STORAGE MEDIUM 审中-公开
公开(公告)号：US20190236412A1
公开(公告)日：2019-08-01
申请号：US16382700
申请日：2019-04-12
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Wei ZHAO , Yabing FENG , Yu LIAO , Junbin LAI , Haixia CHAI , Xuanliang PAN , Lichun LIU
IPC分类号： G06K9/62
CPC分类号： G06K9/6257 , G06K9/6218 , G06K9/6231 , G06K9/6256 , G06K9/6259 , G06K9/6267
摘要： A data processing method and device are provided. The method includes: extracting a plurality of data sets from unlabeled data; and for each data set, creating a plurality of sample sets by assigning labels to data samples in the data set, respectively training, for each sample set created from the data set, a classifier by using the sample set and labeled data, obtaining a sample set that corresponds to a trained classifier with the highest performance, and adding the obtained sample set to a candidate training set. Each sample set includes the first preset number of data samples with respective labels, the labels of the data samples in each sample set constitutes a label combination, and label combinations corresponding to different sample sets are different from each other. The method also includes adding a second preset number of sample sets in the candidate training set to the labeled data.

8. 发明申请

US20180217979A1 TEXT INFORMATION PROCESSING METHOD AND APPARATUS 审中-公开
公开(公告)号：US20180217979A1
公开(公告)日：2018-08-02
申请号：US15940159
申请日：2018-03-29
申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
发明人： Quanchen LIN , Lichun LIU , Jianchun ZHAO
IPC分类号： G06F17/27
CPC分类号： G06F17/2775 , G06F17/2735 , G06F17/277
摘要： The disclosure provides a text information processing method. Training textual data is determined according to text information, and characters and strings are identified from the training textual data. For each of the identified characters, a respective independent probability of appearance among the training textual data is calculated. For each of the identified strings, a respective joint probability of appearance among the training textual data is calculated. Whether a particular string of the identified strings corresponds to a candidate neologism is determined according to independent probabilities of various characters of the particular string and the joint probability of the particular string. Moreover, the candidate neologism is determined as a neologism when the candidate neologism is not in a preset dictionary and a joint probability of the candidate neologism is greater than a preset threshold.

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