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公开(公告)号：US20200065396A1
公开(公告)日：2020-02-27
申请号：US16110434
申请日：2018-08-23
摘要： In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.
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公开(公告)号：US11048705B2
公开(公告)日：2021-06-29
申请号：US15827308
申请日：2017-11-30
发明人： Vijay Dialani , Sahin Cem Geyik , Abhishek Gupta
IPC分类号： G06F16/00 , G06F16/2457 , G06Q50/00 , H04L29/08 , G06Q10/06 , G06N20/00 , G06F16/28 , G06F15/76 , H04L29/06
摘要： Techniques for query intent clustering for automated sourcing are described. In an example embodiment, disclosed is a system comprising a processor, a storage device, and a memory device holding an instruction set executable on the processor to cause the system to perform operations. The system obtains one or more recent hire member profiles used as a basis for a search on member profiles in a social networking service. Additionally, the system extracts one or more attributes from the one or more recent hire member profiles and stores the attributes on the storage device. Moreover, the system identifies skills clusters based on the extracted attributes retrieved from the storage device. Furthermore, the system generates a search query based on the identified skills clusters. Then, a search can be performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles as candidates.
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公开(公告)号：US20200005153A1
公开(公告)日：2020-01-02
申请号：US16021617
申请日：2018-06-28
发明人： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
摘要： Techniques for implementing a learning semantic representations of sparse entities using unsupervised embeddings are disclosed herein. In some embodiments, a computer system accesses corresponding profile data of users indicating at least one entity of a first facet type associated with the user, and generating a graph data structure comprising nodes and edges based on the accessed profile data, with each node corresponding to a different entity indicated by the accessed profile data, and each edge directly connecting a different pair of nodes and indicating a number of users whose profile data indicates both entities of the pair of nodes. The computer system generating a corresponding embedding vector for the entities based on the graph data structure using an unsupervised machine learning algorithm.
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公开(公告)号：US20200004886A1
公开(公告)日：2020-01-02
申请号：US16021639
申请日：2018-06-28
发明人： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
摘要： Techniques for generating supervised embedding representations for search are disclosed herein. In some embodiments, a computer system receives training data comprising query representations including an entity included in a corresponding search query submitted by a querying user, search result representations for each one of the query representations, and user actions for each one of the query representations, and generates a corresponding embedding vector for each one of the at least one entity using a supervised learning algorithm and the received training data. In some example embodiments, the corresponding search result representations for each one of the query representations represents a plurality of candidate users displayed in response to the search queries based on profile data of the candidate users, and the user actions comprise actions by the querying user directed towards at least one candidate user in the search results.
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公开(公告)号：US20210103861A1
公开(公告)日：2021-04-08
申请号：US17126546
申请日：2020-12-18
发明人： Keqing Liang , Wen Pu , Sahin Cem Geyik , Yu Wang , Ying Chen , Yin Zhang , Sumedha K. Swamy
摘要： The disclosed embodiments provide a system for performing dynamic job bidding optimization. During operation, the system obtains historical data containing a time series of interactions with a job. Next, the system uses the historical data to calculate an initial price of a job based on a predicted number of interactions with the job. The system then determines a first dynamic adjustment to the initial price that improves utilization of a budget for the job and a second dynamic adjustment to the initial price that improves a performance of the job. Finally, the system applies the first and second adjustments to the initial price to produce an updated price for the job and delivers the job within an online system based on the updated price.
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公开(公告)号：US20200005149A1
公开(公告)日：2020-01-02
申请号：US16021692
申请日：2018-06-28
发明人： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
摘要： Techniques for applying learning-to-rank with deep learning models for search are disclosed herein. In some embodiments, a computer system trains a ranking model using training data and a loss function, with the ranking model comprising a deep learning model and being configured to generate similarity scores based on a determined level of similarity between profile data of reference candidates users in the training data and reference query data of reference queries in the training data. The computer system receives a target query comprising target query data from a computing device of a target querying user, and then generates a corresponding score for target candidate users based on a determined level of similarity between profile data of the target candidate users and the target query data using the trained ranking model.
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公开(公告)号：US20180232702A1
公开(公告)日：2018-08-16
申请号：US15851584
申请日：2017-12-21
发明人： Vijay Dialani , Sahin Cem Geyik , Xianren Wu , Abhishek Gupta
CPC分类号： G06Q10/1053 , G06F16/93 , G06F16/9535 , G06Q50/01
摘要： Techniques for dynamically altering weights to re-weight candidate features of a candidate search and ranking model in a streaming environment are described. In an embodiment, a disclosed system obtains desired hire documents using a search query specifying parameters. Additionally, the system extracts desired hire-based features from the documents, with the features corresponding to the parameters. Moreover, the system inputs the features to a combined ranking model that is trained by a machine learning algorithm to output a ranking score for each of the documents, with the combined ranking model including weights assigned to each of the features. Furthermore, the system ranks the desired hire documents based on the ranking scores and displays top ranked documents. Then, feedback is received regarding the top ranked documents, and the weights assigned to each of the features are dynamically trained to alter the weights assigned to each of the features based on the feedback.
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公开(公告)号：US20180232421A1
公开(公告)日：2018-08-16
申请号：US15827308
申请日：2017-11-30
发明人： Vijay Dialani , Sahin Cem Geyik , Abhishek Gupta
CPC分类号： G06F16/24575 , G06F15/76 , G06F16/285 , G06N20/00 , G06Q10/06 , G06Q50/01 , H04L67/20 , H04L67/306 , H04L67/42
摘要： Techniques for query intent clustering for automated sourcing are described. In an example embodiment, disclosed is a system comprising a processor, a storage device, and a memory device holding an instruction set executable on the processor to cause the system to perform operations. The system obtains one or more recent hire member profiles used as a basis for a search on member profiles in a social networking service. Additionally, the system extracts one or more attributes from the one or more recent hire member profiles and stores the attributes on the storage device. Moreover, the system identifies skills clusters based on the extracted attributes retrieved from the storage device. Furthermore, the system generates a search query based on the identified skills clusters. Then, a search can be performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles as candidates.
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公开(公告)号：US11017040B2
公开(公告)日：2021-05-25
申请号：US15852523
申请日：2017-12-22
发明人： Vijay Dialani , Sahin Cem Geyik , Abhishek Gupta
IPC分类号： G06F16/9535 , G06Q10/06 , G06F16/30 , G06Q50/00 , G06Q10/10
摘要： Techniques for providing explanations of candidate search queries are described. The queries can be created using query intent clustering in an automated sourcing tool. In an example embodiment, disclosed is a system that obtains one or more current candidate member profiles used as a basis for a search on member profiles in an online system. Additionally, the system extracts one or more attributes from the one or more current candidate member profiles. Moreover, the system identifies query intent clusters based on the extracted one or more attributes. Furthermore, the system generates a search query based on the identified query intent clusters. Next, an explanation of the search query can be displayed on a display device of the system. In some embodiments, the online system hosts a social networking service that includes the member profiles, and the identified query intent clusters include skills clusters.
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公开(公告)号：US10795897B2
公开(公告)日：2020-10-06
申请号：US16021667
申请日：2018-06-28
发明人： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
IPC分类号： G06F16/00 , G06F16/2457 , G06N3/02 , G06F16/248 , G06F16/22 , G06F16/9535
摘要： Techniques for processing search queries are described. Consistent with some embodiments, a computer system generates a profile vector representation for each of several user profiles based on the user profile data of the user profiles, and then stores the vector representations. A subsequent query is processed to generate a query vector representation for the query. A neural network is used to generate a similarity score for each pairing of the query vector representation and a profile vector representation. Finally, some number of user profiles having the highest similarity scores are provided as search results.
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