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公开(公告)号：US11461421B2
公开(公告)日：2022-10-04
申请号：US17136864
申请日：2020-12-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yiming Wang , Xiao Yan , Lin Zhu , Jaewon Yang , Yanen Li , Jacob Bollinger
IPC: G06F16/9535 , G06N3/04 , G06N20/20 , G06F16/36
Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.
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2.发明授权公开(公告)号：US10521482B2
公开(公告)日：2019-12-31
申请号：US15543361
申请日：2017-04-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Liang Zhang , Lin Zhu , Di Wang , Sheng Zhao , Yang Liu , Shu Chen
IPC: G06F17/30 , G06F16/9535 , G06Q50/00 , G06F16/903 , G06N20/00 , G06F16/9536
Abstract: Methods, systems, and computer programs are presented for recommending new connections based on profile similarity and existing interconnections within a social network. One method includes an operation for detecting a request for new connections for a member of the social network, where the profile of the member includes values for certain attributes. Additionally, the method includes operations for identifying members that have at least one equal attribute to the attributes of the member, and for calculating a connection score for each identified member based on the respective values of the identified members attributes. Members are selected from the identified members based on the connection scores, and a ranking score for each selected member is obtained utilizing a machine learning algorithm that utilizes similarity analysis of the attributes to calculate the ranking score. The selected members are presented to the member as the possible new connections based on the ranking scores.
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3.发明申请公开(公告)号：US20190236106A1
公开(公告)日：2019-08-01
申请号：US15543361
申请日：2017-04-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Liang Zhang , Lin Zhu , Di Wang , Sheng Zhao , Yang Liu , Shu Chen
IPC: G06F16/9535 , G06F16/9536 , G06N20/00 , G06Q50/00 , G06F16/903
CPC classification number: G06F16/9535 , G06F16/90335 , G06F16/9536 , G06N20/00 , G06Q30/02 , G06Q50/01
Abstract: Methods, systems, and computer programs are presented for recommending new connections based on profile similarity and existing interconnections within a social network. One method includes an operation for detecting a request for new connections for a member of the social network, where the profile of the member includes values for certain attributes. Additionally, the method includes operations for identifying members that have at least one equal attribute to the attributes of the member, and for calculating a connection score for each identified member based on the respective values of the identified members attributes. Members are selected from the identified members based on the connection scores, and a ranking score for each selected member is obtained utilizing a machine learning algorithm that utilizes similarity analysis of the attributes to calculate the ranking score. The selected members are presented to the member as the possible new connections based on the ranking scores.
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Patent Agency Ranking
公开(公告)号：US11604990B2
公开(公告)日：2023-03-14
申请号：US16902587
申请日：2020-06-16
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Wenjia Ma , Jaewon Yang , Jacob Bollinger , Qi He , Lin Zhu , How Jing
Abstract: 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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公开(公告)号：US20220207099A1
公开(公告)日：2022-06-30
申请号：US17136864
申请日：2020-12-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yiming Wang , Xiao Yan , Lin Zhu , Jaewon Yang , Yanen Li , Jacob Bollinger
IPC: G06F16/9535 , G06N3/04 , G06F16/9536 , G06F16/36 , G06N20/20
Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.
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公开(公告)号：US20210027233A1
公开(公告)日：2021-01-28
申请号：US16521141
申请日：2019-07-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Jaewon Yang , Mikhail Obukhov , Lin Zhu , Joey Bai , Shiqi Wu , Qi He , Farzard Eskafi
Abstract: Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.
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公开(公告)号：US11610161B2
公开(公告)日：2023-03-21
申请号：US16521141
申请日：2019-07-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Jaewon Yang , Mikhail Obukhov , Lin Zhu , Joey Bai , Shiqi Wu , Qi He , Farzard Eskafi
IPC: G06Q30/00 , G06Q10/0631 , G06F17/18 , G06N20/00 , G06Q10/1053
Abstract: Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.
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公开(公告)号：US20180189288A1
公开(公告)日：2018-07-05
申请号：US15125782
申请日：2016-08-01
Applicant: Microsoft Technology Licensing, LLC
Inventor： Liang Zhang , Lin Zhu , Di Wang , Sheng Zhao , Yang Liu , Shu Chen
CPC classification number: G06F16/24578 , G06F16/00 , G06F16/9535 , G06K9/6223 , G06N20/00 , G06Q50/01
Abstract: Methods, systems, and programs are provided for presenting professional content in a user feed. The user feed is populated with industry-wise content, using relevance-driven technologies to select the best relevant industry content, while solving the problem of low-content availability for users with few connections. Professional posts created by users in a social network are identified, where each user is associated with an industry as configured in a profile of the user. A score for each identified professional post is calculated and the professional posts are sorted based on their scores. After detecting a request for data to present in a first user feed to a first user that is associated with a first industry, the server selects professional posts based on the industry of the user that created each post. The selected professional posts are then merged with other types of content and presented in the first user feed.
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