公开(公告)号：US11475085B2

公开(公告)日：2022-10-18

申请号：US16801725

申请日：2020-02-26

申请人： Microsoft Technology Licensing, LLC

发明人： Jianling Zhong ,  Weiwei Guo ,  Lin Guo ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/9535 ,  G06N20/00 ,  G06F16/9538

摘要： Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

公开(公告)号：US11106662B2

公开(公告)日：2021-08-31

申请号：US16584844

申请日：2019-09-26

申请人： Microsoft Technology Licensing, LLC

发明人： Zhong Yi Wan ,  Weiwei Guo ,  Michaeel M. Kazi ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/242 ,  G06N3/04 ,  G06N3/08 ,  G06F16/2457

摘要： In an embodiment, the disclosed technologies include extracting, from at least one search log, session data including at least three semantically related queries and corresponding timestamp data; using the session data, creating a training sequence that includes source query data, context query data, and target query data, the source query data having both a temporal relationship and a lexical relationship to the target query data and the context query data having a temporal relationship to the source query data; creating a learned model by, using a machine learning-based modeling process, learning a mapping of a semantic representation of the context query data and the source query data to a semantic representation of the target query data; in response to a new query, using the learned model to generate at least one recommended query that is semantically related to the new query.

公开(公告)号：US20210263982A1

公开(公告)日：2021-08-26

申请号：US16801725

申请日：2020-02-26

申请人： Microsoft Technology Licensing, LLC

发明人： Jianling Zhong ,  Weiwei Guo ,  Lin Guo ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/9535 ,  G06F16/9538 ,  G06N20/00

摘要： Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

公开(公告)号：US11232154B2

公开(公告)日：2022-01-25

申请号：US16367849

申请日：2019-03-28

申请人： Microsoft Technology Licensing, LLC

发明人： Weiwei Guo ,  Lin Guo ,  Jianling Zhong ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/9032 ,  G06N5/04 ,  G06F16/904 ,  G06F17/16 ,  G06N3/08

摘要： A neural related query generation approach in a search system uses a neural encoder that reads through a source query to build a query intent vector. The approach then processes the query intent vector through a neural decoder to emit a related query. By doing so, the approach gathers information from the entire source query before generating the related query. As a result, the neural encoder-decoder approach captures long-range dependencies in the source query such as, for example, structural ordering of query keywords. The approach can be used to generate related queries for long-tail source queries, including long-tail source queries never before or not recently submitted to the search system.

公开(公告)号：US20210097063A1

公开(公告)日：2021-04-01

申请号：US16584844

申请日：2019-09-26

申请人： Microsoft Technology Licensing, LLC

发明人： Zhong Yi Wan ,  Weiwei Guo ,  Michaeel M. Kazi ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/242 ,  G06F16/2457 ,  G06N3/08 ,  G06N3/04

摘要： In an embodiment, the disclosed technologies include extracting, from at least one search log, session data including at least three semantically related queries and corresponding timestamp data; using the session data, creating a training sequence that includes source query data, context query data, and target query data, the source query data having both a temporal relationship and a lexical relationship to the target query data and the context query data having a temporal relationship to the source query data; creating a learned model by, using a machine learning-based modeling process, learning a mapping of a semantic representation of the context query data and the source query data to a semantic representation of the target query data; in response to a new query, using the learned model to generate at least one recommended query that is semantically related to the new query.

公开(公告)号：US20230124258A1

公开(公告)日：2023-04-20

申请号：US17505519

申请日：2021-10-19

申请人： Microsoft Technology Licensing, LLC

发明人： Xiangyu Zhao ,  Sida Wang ,  Huiji Gao ,  Bo Long ,  Bee-Chung Chen ,  Weiwei Guo ,  Jun Shi

IPC分类号： G06N3/08 ,  G06F5/01

摘要： Methods, systems, and computer programs are presented for determining parameters of neural networks and selecting embedding dimensions for the feature fields. One method includes an operation for initializing parameters of a neural network and weights for embedding sizes for each feature associated with the neural network. The parameters of the neural network and the weights are iteratively optimized. Each optimization iteration comprises training the neural network with current parameters of the neural network to optimize a value of the weights, and training the neural network with current values of the weights to optimize the parameters of the neural network. Further, the method includes operations for selecting embedding sizes for the features based on the optimized values of the weights, and for training the neural network based on the selected embedding sizes for the features to obtain an estimator model. A prediction is generated utilizing the estimator model.

公开(公告)号：US20200311146A1

公开(公告)日：2020-10-01

申请号：US16367849

申请日：2019-03-28

申请人： Microsoft Technology Licensing, LLC

发明人： Weiwei Guo ,  Lin Guo ,  Jianling Zhong ,  Huiji Gao ,  Bo Long

IPC分类号： G06F16/9032 ,  G06N5/04 ,  G06N3/08 ,  G06F17/16 ,  G06F16/904

摘要： A neural related query generation approach in a search system uses a neural encoder that reads through a source query to build a query intent vector. The approach then processes the query intent vector through a neural decoder to emit a related query. By doing so, the approach gathers information from the entire source query before generating the related query. As a result, the neural encoder-decoder approach captures long-range dependencies in the source query such as, for example, structural ordering of query keywords. The approach can be used to generate related queries for long-tail source queries, including long-tail source queries never before or not recently submitted to the search system.