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公开(公告)号：US20230125711A1
公开(公告)日：2023-04-27
申请号：US17511162
申请日：2021-10-26
Applicant: Microsoft Technology Licensing, LLC
Inventor： Shan Li , Baoxu Shi , Jaewon Yang
Abstract: Described herein are techniques for using a graph neural network to encode online job postings as embeddings. First, an input graph is defined by processing one or more rules to discover edges that connect nodes in an input graph, where the nodes of the input graph represent job postings or standardized job attributes, and the edges are determined based on analyzing a log of user activity directed to online job postings. Next, a graph neural network (GNN) is trained based on an edge prediction task. Finally, once trained, the GNN is used to derive node embeddings for the nodes (e.g., job postings) of the input graph, and in some instances, new online job postings not represented in the original input graph.
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公开(公告)号：US11481448B2
公开(公告)日：2022-10-25
申请号：US16836546
申请日：2020-03-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Peide Zhong , Feishe Chen , Weizhi Meng , Wei Kang , Feng Guo , Fei Chen , Jaewon Yang , Qi He
Abstract: During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
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公开(公告)号：US11436542B2
公开(公告)日：2022-09-06
申请号：US16456838
申请日：2019-06-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Mikhail Obukhov , Jaewon Yang , Qi He
Abstract: Techniques for selecting candidates using a personalized model are disclosed herein. In some embodiments, a computer system, for each candidate of a plurality of candidates, generating a corresponding confidence score for a combination of the candidate, a particular viewer, and a particular attribute based on a scoring model, with the corresponding confidence score being configured to indicate a likelihood that the particular viewer will select the corresponding candidate as a preference with respect to the particular attribute. The computer system then selects a subset of the plurality of candidates based on the corresponding confidence scores of the candidates in the subset, and causes the subset of candidates to be displayed on a computing device of the viewer along with a prompting for the viewer to select one of the selected subset of candidates as the preference with respect to the particular attribute.
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Patent Agency Ranking
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：US20200210525A1
公开(公告)日：2020-07-02
申请号：US16235921
申请日：2018-12-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jaewon Yang , Maneesh Varshney , Mikhail Obukhov , Sung Yoon
IPC: G06F17/27 , G06F16/28 , G06F16/332 , G06F16/33 , G06F16/338 , G06N7/00 , G06N20/00
Abstract: Techniques for predicting query language statements from natural language analytic questions. In one embodiment, for example, a computer-implemented method includes: receiving an input natural language analytic question; for each slot of a plurality of slots of a query language statement, using a multi-class classifier, trained on a set of possible values for the slot and a set of natural language analytic seed questions, to predict a probability, for each possible value in the set of possible values for the slot, that the input natural language analytic question is directed to the possible value; for each slot of the plurality of slots, selecting a particular possible value, of the set of possible values for the slot, to fill the slot based on the probability predicted for the slot; and generating the query language statement with the particular possible value selected for each slot of the plurality of slot.
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公开(公告)号：US10586157B2
公开(公告)日：2020-03-10
申请号：US15360576
申请日：2016-11-23
Applicant: Microsoft Technology Licensing, LLC
Inventor： Uri Merhav , How Jing , Jaewon Yang , Dan Shacham
Abstract: In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.
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公开(公告)号：US20190163780A1
公开(公告)日：2019-05-30
申请号：US15826279
申请日：2017-11-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Cagri Ozcaglar , Xianren Wu , Jaewon Yang , Abhishek Gupta , Anish Ramdas Nair
IPC: G06F17/30
Abstract: Techniques for improving search using generalized linear mixed models are disclosed herein. In some embodiments, a computer-implemented method comprises: receiving a search query comprising at least one search term and being associated with a user; extracting features from corresponding profiles of a plurality of candidates; for each one of the candidates, generating a corresponding score based on a generalized linear mixed model comprising a generalized linear query-based model and a random effects user-based model; selecting a subset of candidates from the plurality of candidates based on the corresponding scores; and causing the selected subset of candidates to be displayed to the user in a search results page for the search query.
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