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公开(公告)号：US20230245258A1
公开(公告)日：2023-08-03
申请号：US17590660
申请日：2022-02-01
发明人： Wenjia Ma , Prakruthi Prabhakar , Yiping Yuan , Yanen LI , Tianqi Li , Arvind Murali Mohan
CPC分类号： G06Q50/2057 , G06N20/00
摘要： Methods, systems, and computer programs are provided for presenting career information based on career transitions of members. One method comprises generating, using a machine-learning (ML) model, an embedding for a current job position of a member of an online service. The model is obtained by training a ML program with training data for job transitions of members. Further, the method includes generating, by the ML model, embeddings for career transitions, of members of the online service, that occurred within a predetermined time period. For each career transition, a similarity value is calculated between the embedding of the career transition and the embedding for the current job position. Further, the method includes operations for ranking the career transitions based on the similarity values, generating a career insight for the member based on the ranked career transitions, and causing presentation of the career insight on a user interface.
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公开(公告)号：US20230086724A1
公开(公告)日：2023-03-23
申请号：US17412753
申请日：2021-08-26
发明人： Liwei Wu , Wenjia Ma , Jaewon Yang , Yanen Li
摘要： Techniques for mining training data for use in training a dependency model are disclosed herein. In some embodiments, a computer-implemented method comprises: obtaining training data comprising a plurality of reference skill pairs, each reference skill pair comprising a corresponding first reference skill and a corresponding second reference skill, the plurality of reference skill pairs being included in the training data based on a co-occurrence of the corresponding first and second reference skills for each reference skill pair in the plurality of reference skill pairs, the co-occurrence comprising the corresponding first and second reference skills co-occurring for a same entity; and training a dependency model with a machine learning algorithm using the training data, the dependency model comprising a logistic regression model or a data gradient boosted decision tree (GBDT) model. The dependency model may then be used to identify corresponding dependency relations for a plurality of target skill pairs.
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公开(公告)号：US11816636B2
公开(公告)日：2023-11-14
申请号：US17412753
申请日：2021-08-26
发明人： Liwei Wu , Wenjia Ma , Jaewon Yang , Yanen Li
IPC分类号： G06Q10/1053 , G06N20/20 , G06Q10/0631 , G06N5/01
CPC分类号： G06Q10/1053 , G06N5/01 , G06N20/20 , G06Q10/063112
摘要： Techniques for mining training data for use in training a dependency model are disclosed herein. In some embodiments, a computer-implemented method comprises: obtaining training data comprising a plurality of reference skill pairs, each reference skill pair comprising a corresponding first reference skill and a corresponding second reference skill, the plurality of reference skill pairs being included in the training data based on a co-occurrence of the corresponding first and second reference skills for each reference skill pair in the plurality of reference skill pairs, the co-occurrence comprising the corresponding first and second reference skills co-occurring for a same entity; and training a dependency model with a machine learning algorithm using the training data, the dependency model comprising a logistic regression model or a data gradient boosted decision tree (GBDT) model. The dependency model may then be used to identify corresponding dependency relations for a plurality of target skill pairs.
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公开(公告)号：US11604990B2
公开(公告)日：2023-03-14
申请号：US16902587
申请日：2020-06-16
发明人： Xiao Yan , Wenjia Ma , Jaewon Yang , Jacob Bollinger , Qi He , Lin Zhu , How Jing
摘要： In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.
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