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公开(公告)号：US20200175393A1
公开(公告)日：2020-06-04
申请号：US16206359
申请日：2018-11-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jeffrey Douglas Gee , Rohan Ramanath , Deepak Kumar
IPC: G06N5/04 , G06N3/02 , G06N20/00 , G06F16/9535 , G06F16/335
Abstract: Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system accesses a profile of a first user of an online service stored in a database of the online service, and generates a suggestion for adding a measurable accomplishment to a particular section of a page of the first user based on profile data of the accessed profile using a neural network model, with the neural network model being configured to identify the measurable accomplishment based on the profile data of the accessed profile.
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公开(公告)号：US20200174633A1
公开(公告)日：2020-06-04
申请号：US16206203
申请日：2018-11-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jeffrey Douglas Gee , Rohan Ramanath , Scott Khamphoune , Vasudeva Nagaraja , Deepak Kumar , Himanshu Khurana , Vijay Ramamurthy
IPC: G06F3/0484 , G06F3/0482 , G06F17/24 , G06F17/27 , G06Q10/10
Abstract: Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system identifies job postings published on an online service as corresponding to a type of job based on feature data of each one of the job postings, extracts phrases from the identified job postings based on a corresponding relevancy measurement and a corresponding diversity measurement for each one of the phrases, determines a corresponding section of a page of a user to suggest for placement of the extracted phrase using a placement classifier for each one of the extracted phrases, and generates a corresponding recommendation for the page based on the extracted phrase and the determined section of the extracted phrase for each one of the phrases.
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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
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
Abstract: 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
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
Abstract: 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：US11372940B2
公开(公告)日：2022-06-28
申请号：US15613979
申请日：2017-06-05
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rohan Ramanath , Irina Belousova
IPC: G06F16/9535 , G06N20/00 , G06F16/248 , G06F16/28 , G06F16/901 , G06F16/2457 , G06Q10/06 , G06N5/02
Abstract: Methods, systems, and computer programs are presented for embedding user categories into vectors that capture similarities between the user categories. One method includes an operation for building a graph for a category of attributes for users of a social network, the graph including a vertex for each category value. Connections, built between the graph vertices, have a connection value indicating the number of users to which the category values associated with the vertices have been assigned. Further, a first vector for each category value is obtained based on the graph, where a distance between two category values is a function of the connection value between the corresponding vertices. A user vector, based on the first vectors of the category values, is assigned to each user. A search is performed for a given user based on the user vectors, and results are presented to the given user.
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Patent Agency Ranking
公开(公告)号：US11106979B2
公开(公告)日：2021-08-31
申请号：US16021617
申请日：2018-06-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
IPC: G06N3/08 , G06N3/04 , G06F16/248 , G06F16/901
Abstract: 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：US20200175109A1
公开(公告)日：2020-06-04
申请号：US16206323
申请日：2018-11-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jeffrey Douglas Gee , Rohan Ramanath , Deepak Kumar
Abstract: Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system receives a plurality of phrases, and then, for each one of the plurality of phrases, selects a corresponding section of a page of a first user to suggest for placement of the phrase from amongst a plurality of sections using a placement classifier, and generates a corresponding recommendation for the page of a first user based on the phrase and the determined corresponding section of the page of the first user, with the recommendation comprising a suggested addition of the phrase to the determined corresponding section of the page of the first user.
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公开(公告)号：US20200175108A1
公开(公告)日：2020-06-04
申请号：US16206292
申请日：2018-11-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jeffrey Douglas Gee , Rohan Ramanath , Deepak Kumar
Abstract: Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system receives a plurality of phrases for a type of job, selects a group of phrases from the plurality of phrases based on a corresponding relevancy measurement and a corresponding diversity measurement for each phrase in the selected group of phrases, and generates a recommendation for a page of a first user based on the selected group of phrases, with the recommendation comprising a suggested addition of the selected group of phrases to the page of the first user.
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公开(公告)号：US20200005134A1
公开(公告)日：2020-01-02
申请号：US16021654
申请日：2018-06-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rohan Ramanath , Gungor Polatkan , Qi Guo , Cagri Ozcaglar , Krishnaram Kenthapadi , Sahin Cem Geyik
Abstract: Techniques for generating supervised embedding representations using unsupervised embedding representations and deep semantic structured models for search are disclosed herein. In some embodiments, a computer system generates a graph data structure based on accessed profile data, generates an initial embedding vector using an unsupervised machine learning algorithm, receiving training data comprising query representations, search result representations, and user actions, with each one of the plurality of query representations comprising the initial embedding vector, and generates a final embedding vector using a supervised learning algorithm and the received training data.
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公开(公告)号：US11386365B2
公开(公告)日：2022-07-12
申请号：US16144012
申请日：2018-09-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Sanjay Sachdev , Arjun K. Kulothungun , Rohan Ramanath , Deepak Dileep Kumar
IPC: G06F16/20 , G06Q10/06 , G06N20/00 , G06F16/9535 , G06F16/903
Abstract: The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains parameters associated with a query for a ranking of candidates for an opportunity, wherein the parameters include a candidate and the opportunity. Next, the system matches one or more of the parameters to a fixed number of quantile thresholds calculated from a distribution of scores for the candidates. The system then estimates, based on the fixed number of quantile thresholds, a quantile for a score of the candidate. Finally, the system outputs a position of the candidate within the ranking based on the estimated quantile.
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