公开(公告)号：US20230410054A1

公开(公告)日：2023-12-21

申请号：US17842128

申请日：2022-06-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Shaghayegh Gharghabi ,  Huichao Xue

IPC: G06Q10/10 ,  G06F16/9535 ,  G06N20/00

CPC classification number: G06Q10/1053 ,  G06F16/9535 ,  G06N20/00

Abstract: Described herein is a candidate selection technique for an online recommendation system or service. Upon receiving a request to generate recommendations for an end-user, attributes of the end-user are obtained. The end-user attributes are then provided as an input to a trained machine learned model, which generates for each attribute a score indicating the predictive power of the attribute in recommending a relevant content item (e.g., an online job posting). Then, a weighted-OR query is derived from a combination of attributes having scores that exceed a predetermined threshold. The query is expressed, such that, content items satisfying the query include at least “k” attributes specified by the query.

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

IPC: G06Q10/06 ,  G06N99/00

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.

公开(公告)号：US11797619B2

公开(公告)日：2023-10-24

申请号：US16838773

申请日：2020-04-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Qing Duan ,  Junrui Xu ,  Huichao Xue ,  Jianqiang Shen

IPC: G06F3/0482 ,  G06F16/9535 ,  G06N3/08 ,  G06F18/214 ,  G06F18/22

CPC classification number: G06F16/9535 ,  G06F3/0482 ,  G06F18/2155 ,  G06F18/22 ,  G06N3/08

Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.

公开(公告)号：US20230281207A1

公开(公告)日：2023-09-07

申请号：US17592128

申请日：2022-02-03

Applicant: Microsoft Technology Licensing, LLC

Inventor： Huichao Xue ,  Xiaoqing Wang ,  Chao Wang

IPC: G06F16/2457

CPC classification number: G06F16/24578

Abstract: In an example embodiment, machine learning is used to train a machine-learned model that projects each entity, title pair into a single number, called a seniority score, to represent the career progression needed for that position. For example, company A’s “software engineer” and company B’s “senior software engineer” can be represented as two separate numbers, one being p (company A, software engineer) and the other being p (company B, senior software engineer) on the same axis. This allows a comparison to be made about the absolute levels of each title despite their potential different meanings at different entities.

公开(公告)号：US20200151672A1

公开(公告)日：2020-05-14

申请号：US16185262

申请日：2018-11-09

Applicant: Microsoft Technology Licensing, LLC

Inventor： Huichao Xue ,  Ye Yuan ,  Girish Kathalagiri Somashekariah ,  Ada Cheuk Ying Yu

IPC: G06Q10/10 ,  G06Q50/00 ,  G06N99/00 ,  G06N5/04

Abstract: The disclosed embodiments provide a system that ranks job recommendations based on title preferences. During operation, the system determines features related to applications for jobs by a candidate, wherein the features include a title preference for the candidate and a similarity between a first set of attribute values for the candidate and a second set of attribute values for a job. Next, the system applies a machine learning model to the features to produce scores representing likelihoods of the candidate applying to the jobs. The system then generates a ranking of the jobs by the scores. Finally, the system outputs, to the candidate, at least a portion of the ranking as a set of recommendations.

公开(公告)号：US11443255B2

公开(公告)日：2022-09-13

申请号：US16185365

申请日：2018-11-09

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ye Yuan ,  Girish Kathalagiri Somashekariah ,  Huichao Xue ,  Ada Cheuk Ying Yu

IPC: G06Q10/06 ,  G06Q10/10 ,  G06N5/04 ,  G06N20/00

Abstract: The disclosed embodiments provide a system for performing activity-based inference of title preferences. During operation, the system determines features and labels related to first title preferences for jobs sought by a first set of candidates. Next, the system inputs the features and the labels as training data for a machine learning model. The system then applies the machine learning model to additional features for a second set of candidates to produce predictions of second title preferences for the second set of candidates. Finally, the system stores the predictions in association with the second set of candidates.

公开(公告)号：US11238124B2

公开(公告)日：2022-02-01

申请号：US16553375

申请日：2019-08-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Huichao Xue

IPC: G06F7/02 ,  G06F16/00 ,  G06F16/9538 ,  G06N20/20 ,  G06F16/9035 ,  G06F16/9535

Abstract: Methods, systems, and computer programs are presented for search optimization based on relevant-parameter selection. One method includes an operation for training a machine-learning program with information about users of an online service to generate a machine-learning model that calculates parameter preference scores for a plurality of parameters. Further, the method includes operations for detecting a job search for a user, identifying user parameters associated with the user, and calculating, by the machine-learning model, the parameter preference scores for the user parameters. Further, search parameters are determined by selecting a predetermined number of user parameters base on the parameter preference scores. A search of a job-postings database is performed with the search parameters, and the results are presented on a display.

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.

公开(公告)号：US10789312B2

公开(公告)日：2020-09-29

申请号：US15828915

申请日：2017-12-01

Applicant: Microsoft Technology Licensing, LLC

Inventor： Dhruv Arya ,  Kevin Kao ,  Huichao Xue

IPC: G06F16/9535 ,  G06F16/9038 ,  G06Q50/00

Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving, from a member of an online networking service, a query for one or more available employment positions; executing the query, at a database of employment positions, to retrieve the one or more available employment positions; filtering results of the query according to one or more facets; generating an electronic user interface to display the filtered results; and allowing the member to adjust the facets using the electronic user interface.

公开(公告)号：US20250013651A1

公开(公告)日：2025-01-09

申请号：US18892931

申请日：2024-09-23

Applicant: Microsoft Technology Licensing, LLC

Inventor： Huichao Xue ,  Xiaoqing Wang ,  Chao Wang

IPC: G06F16/2457

Abstract: In an example embodiment, machine learning is used to train a machine-learned model that projects each entity, title pair into a single number, called a seniority score, to represent the career progression needed for that position. For example, company A's “software engineer” and company B's “senior software engineer” can be represented as two separate numbers, one being p (company A, software engineer) and the other being p (company B, senior software engineer) on the same axis. This allows a comparison to be made about the absolute levels of each title despite their potential different meanings at different entities.