公开(公告)号：US20230196070A1

公开(公告)日：2023-06-22

申请号：US17556218

申请日：2021-12-20

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yuan Sun ,  Ye Tu ,  Ying Han ,  Chun Lo ,  Shaunak Chatterjee ,  Vrishti Gulati

IPC: G06N3/04 ,  G06N20/20

CPC classification number: G06N3/0454 ,  G06N20/20 ,  G06N3/0472

Abstract: In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.

公开(公告)号：US10769227B2

公开(公告)日：2020-09-08

申请号：US16241649

申请日：2019-01-07

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ye Tu ,  Yiping Yuan ,  Chun Lo ,  Shaunak Chatterjee ,  Yijie Wang

IPC: G06F15/16 ,  G06F16/954 ,  G06F16/953 ,  G06Q50/00 ,  G06N20/00 ,  H04L29/08

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

公开(公告)号：US20250005346A1

公开(公告)日：2025-01-02

申请号：US18216237

申请日：2023-06-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Chun Lo ,  Lu Chen ,  Ajith Muralidharan ,  Lingjie Weng ,  Mohan Premchand Bhambhani ,  Zichu Li

IPC: G06N3/08 ,  H04L67/1396 ,  H04L67/50

Abstract: In an example embodiment, a user's session sequence data is utilized to provide a universal member representation that achieves one or more of the following goals: 1. Provides a user-level representation that enables the prediction of future actions based on historical interactions within different domains 2. Provides a user representation that allows better clarification of user intent (e.g., network builder, job seeker, profile scraper, etc.) 3. Members with similar/behaviors/intent are easily identified 4. Less sensitivity to activity levels of members.

公开(公告)号：US20200218770A1

公开(公告)日：2020-07-09

申请号：US16241649

申请日：2019-01-07

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ye Tu ,  Yiping Yuan ,  Chun Lo ,  Shaunak Chatterjee ,  Yijie Wang

IPC: G06F16/954 ,  G06F16/953 ,  H04L29/08 ,  G06N20/00 ,  G06Q50/00

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

公开(公告)号：US20240412299A1

公开(公告)日：2024-12-12

申请号：US18371142

申请日：2023-09-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Aman Gupta ,  Xincen Yu ,  Ning Jin ,  Kuan Chen ,  Madhura Anil Deo ,  Gina Paola Rangel ,  Smriti R. Ramakrishnan ,  Xiaoxi Zhao ,  Chun Lo ,  Arvind Murali Mohan ,  Hongbo Zhao ,  Shifu Wang ,  Jim Chang

IPC: G06Q30/0204 ,  G06N3/08 ,  G06Q10/1053 ,  G06Q50/00

Abstract: In an example embodiment, a deep machine learning model ranks cohorts of users as well as cohorts of products in a single ranking. When utilized to determine which cohort members to display to a user, the system selects one user cohort and one product cohort as the “best” (e.g., the top ranked user cohort and the top ranked product cohort). This ranking may be based on a number of contextual and non-contextual features, including viewer features (characteristics of the user operating the user interface), viewee features (characteristics of or related to the litem that the user is viewing, such as the characteristics of another user whose profile the user is viewing), and viewer-viewee relationship features (indications about how the viewer and viewee are related, such as common schools, locations, places of employment, etc.).

公开(公告)号：US11537911B2

公开(公告)日：2022-12-27

申请号：US16775620

申请日：2020-01-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Chun Lo ,  Emilie De Longueau ,  Ankan Saha ,  Shaunak Chatterjee ,  Ye Tu

IPC: G06N20/00 ,  G06N5/04

Abstract: Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.

公开(公告)号：US20210232942A1

公开(公告)日：2021-07-29

申请号：US16775620

申请日：2020-01-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Chun Lo ,  Emilie De Longueau ,  Ankan Saha ,  Shaunak Chatterjee ,  Ye Tu

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.