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公开(公告)号：US11205155B2
公开(公告)日：2021-12-21
申请号：US16429365
申请日：2019-06-03
Applicant: Microsoft Technology Licensing, LLC
Inventor： Swanand Wakankar , Meng Meng , Cagri Ozcaglar , Vitaly Abdrashitov
Abstract: Methods, systems, and computer programs are presented for improved search methods based on career transition embeddings. One method includes an operation for generating career transition vectors for members of an online service, each career transition vector comprising identifiers associated with the career transitions of each member. The method further includes operations for performing a similarity analysis of the career transition vectors to generate an embedding vector for each identifier, detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
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公开(公告)号：US20200210929A1
公开(公告)日：2020-07-02
申请号：US16234377
申请日：2018-12-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Haifeng Zhao
IPC: G06Q10/06 , G06N5/04 , G06N20/00 , G06Q10/10 , G06F16/9535
Abstract: The disclosed embodiments provide a system for performing sequence modeling for searches. During operation, the system obtains a sequence of jobs associated with activity by a member of an online system. Next, the system applies a word embedding model of a set of job histories to attributes of individual jobs in the sequence of jobs to produce embeddings for the individual jobs. The system then generates a set of power means from the embeddings. Finally, the system outputs the set of power means as an encoded representation of the sequence of jobs, wherein the set of power means is used in generating job recommendations related to the member.
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公开(公告)号：US11544308B2
公开(公告)日：2023-01-03
申请号：US16367820
申请日：2019-03-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Gheorghe Muresan , Ada Cheuk Ying Yu
Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.
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公开(公告)号：US11308426B2
公开(公告)日：2022-04-19
申请号：US16234377
申请日：2018-12-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Haifeng Zhao
IPC: G06Q10/06 , G06N5/04 , G06F16/9535 , G06Q10/10 , G06N20/00
Abstract: The disclosed embodiments provide a system for performing sequence modeling for searches. During operation, the system obtains a sequence of jobs associated with activity by a member of an online system. Next, the system applies a word embedding model of a set of job histories to attributes of individual jobs in the sequence of jobs to produce embeddings for the individual jobs. The system then generates a set of power means from the embeddings. Finally, the system outputs the set of power means as an encoded representation of the sequence of jobs, wherein the set of power means is used in generating job recommendations related to the member.
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Patent Agency Ranking
公开(公告)号：US20200311162A1
公开(公告)日：2020-10-01
申请号：US16367716
申请日：2019-03-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Junrui Xu , Meng Meng , Girish Kathalagiri Somashekariah , Huichao Xue , Varun Mithal , Ada Cheuk Ying Yu
IPC: G06F16/9536 , G06F16/903 , G06N20/00
Abstract: The disclosed embodiments provide a system for selecting recommendations based on title transition embeddings. During operation, the system obtains a word embedding model of a set of job histories. Next, the system calculates similarities between pairs of the embeddings produced by the word embedding model from attributes associated with titles in the set of job histories. The system then identifies, based on the similarities, job titles with high similarity to a current title of the candidate. Finally, the system outputs the job titles for use in selecting job recommendations for the candidate.
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6.发明授权公开(公告)号：US12190209B2
公开(公告)日：2025-01-07
申请号：US17313560
申请日：2021-05-06
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Daniel Sairom Krishnan Hewlett , Sriram Vasudevan , Vitaly Abdrashitov
IPC: G06N20/00 , G06F16/951 , G06F16/955 , G06F18/2113 , H04L67/02
Abstract: Techniques for incorporating sequence encoders into machine-learned models where the sequence encoders operate on bag of words (BOW) input are provided. Tokens that are associated with online activities of an entity are identified. Machine-learned embeddings that correspond to the tokens are identified. Based on one or more ordering criteria that are independent of the temporal occurrence of the online activities of the entity, an order of the machine-learned embeddings is determined. Based on the order, the machine-learned embeddings are inputted to a sequence encoder that generates output. Based on the output, a machine learned model that includes the sequence encoder generates a score. A content item is selected based on the score. The content item is transmitted over a computer network to a computing device.
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公开(公告)号：US20220284028A1
公开(公告)日：2022-09-08
申请号：US17195261
申请日：2021-03-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Daniel Sairom Krishnan Hewlett , Sriram Vasudevan
IPC: G06F16/2457 , G06Q10/10 , G06N3/08
Abstract: Described herein is machine learning model comprising a neural network that is trained to generate a ranking score for an online job posting. The neural network takes as input a variety of input features, including at least a first input feature that is an encoded representation of a search query as generated by a first Transformer encoder, an encoded representation of a job title as generated by a second Transformer encoder, and an encoded representation of a company name as generated by a third Transformer encoder. Once a plurality of online job postings are ranked, some subset of the plurality are presented in a user interface, ordered based on their respective ranking scores.
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公开(公告)号：US20200311112A1
公开(公告)日：2020-10-01
申请号：US16367820
申请日：2019-03-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Meng Meng , Gheorghe Muresan , Ada Cheuk Ying Yu
IPC: G06F16/33 , G06F16/31 , G06F16/9535 , G06N20/00 , G06Q10/06
Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.
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公开(公告)号：US20200151647A1
公开(公告)日：2020-05-14
申请号：US16185272
申请日：2018-11-09
Applicant: Microsoft Technology Licensing, LLC
Inventor： Girish Kathalagiri Somashekariah , Huichao Xue , Ye Yuan , Meng Meng , Ada Cheuk Ying Yu
Abstract: The disclosed embodiments provide a system for recommending jobs based on title transition embeddings. During operation, the system obtains a word embedding model of job histories of members of an online network. Next, the system applies the word embedding model to a first set of attributes associated with a title of a candidate to produce a first embedding. The system also applies the word embedding model to a second set of attributes associated with a job title of a job to produce a second embedding. The system then calculates a similarity between the first and second embeddings. Finally, the system outputs the similarity for use in recommending the job to the candidate.
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