公开(公告)号：US20220100746A1

公开(公告)日：2022-03-31

申请号：US17038395

申请日：2020-09-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Wenxiang Chen ,  William Tang ,  Runfang Zhou ,  Tanvi Sudarshan Motwani ,  Jeremy Lwanga ,  Sara Smoot Gerrard ,  Daniel Sairom Krishnan Hewlett ,  Alexandre Patry ,  Songtao Guo ,  Sai Krishna Bollam

IPC: G06F16/242 ,  G06F16/9035 ,  G06F16/9032 ,  G06K9/62

Abstract: Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

公开(公告)号：US20210406838A1

公开(公告)日：2021-12-30

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: G06Q10/10 ,  G06N5/04 ,  G06K9/62 ,  G06F16/958 ,  G06F16/9535 ,  G06Q50/00

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：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：US11580099B2

公开(公告)日：2023-02-14

申请号：US17038395

申请日：2020-09-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Wenxiang Chen ,  William Tang ,  Runfang Zhou ,  Tanvi Sudarshan Motwani ,  Jeremy Lwanga ,  Sara Smoot Gerrard ,  Daniel Sairom Krishnan Hewlett ,  Alexandre Patry ,  Songtao Guo ,  Sai Krishna Bollam

IPC: G06F16/00 ,  G06F16/242 ,  G06K9/62 ,  G06F16/9032 ,  G06F16/9035

Abstract: Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

公开(公告)号：US11503127B2

公开(公告)日：2022-11-15

申请号：US17036856

申请日：2020-09-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Tao Cai ,  Tianchen Yu ,  Sara Smoot Gerrard ,  Sanjay Agarwal ,  Meilin Yang ,  Zhongwei Jiang

IPC: G06F15/173 ,  H04L67/51 ,  H04L67/30 ,  H04L67/568

Abstract: Techniques for performing prefetching for a ranking service in a microservice architecture are provided. In one technique, in response to receiving a content request, an entity identifier of an entity associated with the content request is determined, a host of a second service that is different than the first service is determined. The first service sends the entity identifier to the host of the second service. The second service retrieves entity feature data that is associated with the entity identifier. The first service identifies a set of content delivery campaigns, identifies the host of the second service, and sends the identity of the set of content delivery campaigns to the host of the second service. The host of the second service determines a ranking of the set of content delivery campaigns, a subset thereof is selected, and data about each selected campaign is transmitted over a computer network.