公开(公告)号：US12223554B2

公开(公告)日：2025-02-11

申请号：US16912245

申请日：2020-06-25

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rohan Ramanath ,  Konstantin Salomatin ,  Jeffrey Douglas Gee ,  Onkar Anant Dalal ,  Gungor Polatkan ,  Sara Smoot Gerrard ,  Deepak Kumar ,  Rupesh Gupta ,  Jiaqi Ge ,  Lingjie Weng ,  Shipeng Yu

IPC: G06Q50/00 ,  G06F16/9535 ,  G06F16/958 ,  G06F18/214 ,  G06N5/04 ,  G06Q10/1053

Abstract: In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.

公开(公告)号：US10885275B2

公开(公告)日：2021-01-05

申请号：US16206323

申请日：2018-11-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jeffrey Douglas Gee ,  Rohan Ramanath ,  Deepak Kumar

IPC: G06F40/289 ,  G06Q50/00 ,  H04L29/08

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.

公开(公告)号：US10795897B2

公开(公告)日：2020-10-06

申请号：US16021667

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: G06F16/00 ,  G06F16/2457 ,  G06N3/02 ,  G06F16/248 ,  G06F16/22 ,  G06F16/9535

Abstract: 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.

公开(公告)号：US20200004835A1

公开(公告)日：2020-01-02

申请号：US16021667

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: G06F17/30 ,  G06N3/02

Abstract: Techniques for generating candidates for search using a scoring and retrieval architecture and deep semantic features are disclosed herein. In some embodiments, a computer system generates a profile vector representation for user profiles based profile data, stores the profile vector representations, receives a query subsequent to the storing of the profile vector representations, generates a query vector representation for the query, retrieves the stored profile vector representations of the user profiles based on the receiving of the query, generates a corresponding score for pairings of the user profiles and the query based on a determined level of similarity between the profile vector representation of the user profiles and the query vector representation, and causes an indication of at least a portion of the user profiles to be displayed as search results for the query based on the generated scores of the user profiles.

公开(公告)号：US10809892B2

公开(公告)日：2020-10-20

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: G06F17/00 ,  G06F3/0484 ,  G06F3/0482 ,  G06Q10/10 ,  G06F40/166 ,  G06F40/289

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.

公开(公告)号：US20200175476A1

公开(公告)日：2020-06-04

申请号：US16206264

申请日：2018-11-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jeffrey Douglas Gee ,  Rohan Ramanath ,  Deepak Kumar

IPC: G06Q10/10 ,  H04L29/08

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 job postings published on an online service; determines that a subset of the plurality of the job postings satisfies a similarity criteria based on corresponding feature data of each job posting in the subset, selects the subset of the plurality of job postings based on the determining that the subset satisfies the similarity criteria, and generates a recommendation for a page of a first user based on the selected subset of job postings, the recommendation comprising a suggested addition of content to the page of the first user.

公开(公告)号：US20200005149A1

公开(公告)日：2020-01-02

申请号：US16021692

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 ,  G06F17/30 ,  G06N3/04

Abstract: 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.

公开(公告)号：US10409651B2

公开(公告)日：2019-09-10

申请号：US15706225

申请日：2017-09-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Wen Pu ,  Liqin Xu ,  Rohan Ramanath ,  Kun Liu

IPC: G06F9/46 ,  G06F9/50 ,  G06F16/23

Abstract: Techniques for incremental workflow execution are provided. In one technique, a computing job in a workflow identifies an input path that indicates a first location from which the computing job is to read input data. The computing job identifies an output path that indicates a second location to which the computing job is to write output data. The computing job performs a comparison between the input path and the output path. Based on the comparison, the computing job determines whether to read the input data from the first location. If the input path does not correspond to the output path, then the computing job reads the input data from the first location, generates particular output data based on the input data, and writes the particular output data to the second location. The computing job ceases to execute if the input path corresponds to the output path.

公开(公告)号：US20190251422A1

公开(公告)日：2019-08-15

申请号：US15941314

申请日：2018-03-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rohan Ramanath ,  Gungor Polatkan ,  Liqin Xu ,  Bo Hu ,  Shan Zhou ,  Harold Hotelling Lee

IPC: G06N3/04 ,  G06F17/30

CPC classification number: G06N3/0454 ,  G06F16/24578 ,  G06N3/04 ,  G06N3/08

Abstract: Techniques for implementing a deep neural network architecture for search are disclosed herein. In some embodiments, the deep neural network architecture comprises: an item neural network configured to, for each one of a plurality of items, generate an item vector representation based on item data of the one of the plurality of items; a query neural network configured to generate a query vector representation for a query based on the query, the query neural network being distinct from the item neural network; and a scoring neural network configured to, for each one of the plurality of items, generate a corresponding score for a pairing of the one of the plurality of items and the query based on the item vector representation of the one of the plurality of items and the query vector representation, the scoring neural network being distinct from the item neural network and the query neural network.

公开(公告)号：US20200175455A1

公开(公告)日：2020-06-04

申请号：US16206729

申请日：2018-11-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jeffrey Douglas Gee ,  Rohan Ramanath ,  Deepak Kumar ,  Vasudeva Nagaraja

IPC: G06Q10/06 ,  G06Q10/10 ,  G06F17/18

Abstract: A skills classification system is configured to calculate, for a skill from the skills database, industry-specific probabilities for the industries associated with the skill. An industry-specific probability for an industry with respect to a skill is the probability of that skill being a required skill for a job associated with that industry. The skills classification system also calculates an industry-agnostic probability with respect to that same skill, which is the probability of the skill being a required skills for any job regardless of the industry. Based on the distance between the set of industry-specific probabilities for the industries associated with the skill and the industry-agnostic probability, the skills classification system calculates a score for the skill. This score is used to determine whether the skill should be tagged with a soft skill identifier or a hard skill identifier.