公开(公告)号：US10586157B2

公开(公告)日：2020-03-10

申请号：US15360576

申请日：2016-11-23

申请人： Microsoft Technology Licensing, LLC

发明人： Uri Merhav ,  How Jing ,  Jaewon Yang ,  Dan Shacham

IPC分类号： G06N5/02 ,  G06N20/00 ,  G06Q50/00 ,  G06F16/22

摘要： 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.

公开(公告)号：US11604990B2

公开(公告)日：2023-03-14

申请号：US16902587

申请日：2020-06-16

申请人： Microsoft Technology Licensing, LLC

发明人： Xiao Yan ,  Wenjia Ma ,  Jaewon Yang ,  Jacob Bollinger ,  Qi He ,  Lin Zhu ,  How Jing

IPC分类号： G06N3/08 ,  G06F9/48 ,  H04L67/50 ,  G06F30/27 ,  G06N3/02

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.

公开(公告)号：US20190130281A1

公开(公告)日：2019-05-02

申请号：US15799396

申请日：2017-10-31

申请人： Microsoft Technology Licensing, LLC

发明人： Jaewon Yang ,  Qi He ,  How Jing ,  Bee-Chung Chen ,  Liangyue Li

IPC分类号： G06N5/02

摘要： Techniques for predicting a next company and next title of a user are disclosed herein. In some embodiments, an encoder is used for encoding a representation of the user's profile. The encoding includes accessing discrete entities comprising context information included in the user's profile, constructing a plurality of embedding vectors from the context information, and generating a context vector from the plurality of embedding vectors. The plurality of embedding vectors including a skill embedding vector, a school embedding vector, and a location embedding vector. A decoder is for decoding a career path from the context vector. The decoding includes applying a long short-term memory (LSTM) model to the context vector to generate perform the prediction of the user's next company and next title for presentation in a user interface.