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公开(公告)号：US20200311146A1
公开(公告)日：2020-10-01
申请号：US16367849
申请日：2019-03-28
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.
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公开(公告)号：US20190197158A1
公开(公告)日：2019-06-27
申请号：US15849723
申请日：2017-12-21
发明人： Hamed Firooz , Lin Guo
摘要： Techniques for performing a database search using a rewritten and annotated query are disclosed herein. In example embodiments, a profile lexicon is generated from a set of raw user profiles. A click-through lexicon is generated from a raw query log. A machine-learning model is trained for entity prediction using selected data. Query tagger data is generated using the profile lexicon, the click-through lexicon, and the machine-learning model. A raw query is received. The raw query is rewritten as an annotated query based on the generated query tagger data. A search of a database is performed using the annotated query. Results of the search are returned in response to the receiving of the raw query for presentation in a user interface.
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公开(公告)号：US20210263982A1
公开(公告)日：2021-08-26
申请号：US16801725
申请日：2020-02-26
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.
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公开(公告)号：US10713316B2
公开(公告)日：2020-07-14
申请号：US15299100
申请日：2016-10-20
发明人： Lin Guo , Abhimanyu Lad , Ganesh Venkataraman
IPC分类号： G06F16/9535 , G06F16/9038 , G06F16/9032 , G06F16/35 , G06F40/242 , G06F40/279
摘要： This disclosure relates to systems and methods for searching names using name clusters. A method includes receiving names, generating a plurality of phonetic cluster identifiers, forming a plurality of name clusters by grouping the names having an equivalent cluster id, removing names from the respective name clusters that differ from a root name by more than either a particular spelling of a phonetic sound or a specific member's reformulation according to a reformulation dictionary, and suggesting one or more names by generating a phonetic cluster id for the received name using the database of phonetic associations and returning names found in the name cluster that matches the phonetic cluster id.
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公开(公告)号：US11475085B2
公开(公告)日：2022-10-18
申请号：US16801725
申请日：2020-02-26
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.
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公开(公告)号：US10762083B2
公开(公告)日：2020-09-01
申请号：US15849723
申请日：2017-12-21
发明人： Hamed Firooz , Lin Guo
IPC分类号： G06F16/2453 , G06N20/00 , G06F16/31 , G06F16/901
摘要： Techniques for performing a database search using a rewritten and annotated query are disclosed herein. In example embodiments, a profile lexicon is generated from a set of raw user profiles. A click-through lexicon is generated from a raw query log. A machine-learning model is trained for entity prediction using selected data. Query tagger data is generated using the profile lexicon, the click-through lexicon, and the machine-learning model. A raw query is received. The raw query is rewritten as an annotated query based on the generated query tagger data. A search of a database is performed using the annotated query. Results of the search are returned in response to the receiving of the raw query for presentation in a user interface.
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公开(公告)号：US20190130023A1
公开(公告)日：2019-05-02
申请号：US15908467
申请日：2018-02-28
发明人： Saurabh Kataria , Lin Guo , Ada Cheuk Ying Yu , Dhruv Arya
IPC分类号： G06F17/30
摘要： Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.
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公开(公告)号：US11232154B2
公开(公告)日：2022-01-25
申请号：US16367849
申请日：2019-03-28
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.
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公开(公告)号：US10747793B2
公开(公告)日：2020-08-18
申请号：US15908467
申请日：2018-02-28
发明人： Saurabh Kataria , Lin Guo , Ada Cheuk Ying Yu , Dhruv Arya
IPC分类号： G06F16/338 , G06N3/04 , G06F16/33 , G06F16/332
摘要： Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.
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