公开(公告)号：US20220092703A1

公开(公告)日：2022-03-24

申请号：US17028672

申请日：2020-09-22

Applicant: Microsoft Technology Licensing, LLC

Inventor： Aastha Nigam ,  Ye Tu ,  Shaunak Chatterjee

IPC: G06Q50/00 ,  G06Q30/02 ,  G06F16/2457 ,  G06F16/248

Abstract: Systems and methods for engagement mapping based on counterfactual experiments are provided. In example embodiments, a network system receives parameters for one or more counterfactual experiments or tests. Based on the parameters, the network system selects one or more users of a social network platform to subject to the test(s) and selects edges of a social network of each of the one or more users to block. The network system then filters out notifications and feed items from the selected edges of the one or more users. Behavior data of the one or more users based on the filtering out of the notifications and feed items is aggregated, whereby the behavior data indicates engagement of the one or more users on the social networking platform based on the filtering of the notifications and feed items. Recommendations are derived based on the aggregated behavior data and presented to the users.

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：US11328369B2

公开(公告)日：2022-05-10

申请号：US17028672

申请日：2020-09-22

Applicant: Microsoft Technology Licensing, LLC

Inventor： Aastha Nigam ,  Ye Tu ,  Shaunak Chatterjee

IPC: G06F16/23 ,  G06Q50/00 ,  G06Q30/02 ,  G06F16/248 ,  G06F16/2457

Abstract: Systems and methods for engagement mapping based on counterfactual experiments are provided. In example embodiments, a network system receives parameters for one or more counterfactual experiments or tests. Based on the parameters, the network system selects one or more users of a social network platform to subject to the test(s) and selects edges of a social network of each of the one or more users to block. The network system then filters out notifications and feed items from the selected edges of the one or more users. Behavior data of the one or more users based on the filtering out of the notifications and feed items is aggregated, whereby the behavior data indicates engagement of the one or more users on the social networking platform based on the filtering of the notifications and feed items. Recommendations are derived based on the aggregated behavior data and presented to the users.

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：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.

公开(公告)号：US20200311745A1

公开(公告)日：2020-10-01

申请号：US16370224

申请日：2019-03-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ye Tu ,  Kinjal Basu ,  Jinyun Yan ,  Shaunak Chatterjee ,  Birjodh S. Tiwana

IPC: G06Q30/02 ,  G06N5/02 ,  G06N7/00

Abstract: Technologies for optimizing content delivery to end-users are provided. Disclosed techniques include storing results of an online experiment with respect to a set of users and determining a plurality of distinct subsets of users based upon the results of the experiment. Users within each of the plurality of distinct subsets may be identified based on metric impacts of the online experiment. For each distinct subset and each associated model parameter, a utility value that represents effectiveness of the model parameter, with respect to an objective, may be determined. An objective optimization model may be used to automatically determine probabilities for each of the model parameters associated with each distinct subset. Users of a second set of users may be assigned to a distinct subset and associated model parameters may be applied to a content delivery strategies of the second set of users.

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.