公开(公告)号：US20230394084A1

公开(公告)日：2023-12-07

申请号：US17830067

申请日：2022-06-01

Applicant: Microsoft Technology Licensing, LLC.

Inventor： Zhanglong Liu ,  Ankan Saha ,  Yiou Xiao ,  Kathryn L. Evans ,  Aastha Jain ,  Aastha Nigam

IPC: G06F16/901 ,  G06N3/04 ,  G06N3/08 ,  G06F16/28

CPC classification number: G06F16/9024 ,  G06N3/0445 ,  G06N3/08 ,  G06F16/288

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

公开(公告)号：US20210216944A1

公开(公告)日：2021-07-15

申请号：US16743486

申请日：2020-01-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Siyuan Gao ,  Yiou Xiao ,  Parag Agrawal ,  Aastha Jain

IPC: G06Q10/06 ,  H04L29/08 ,  G06Q50/00 ,  G06N20/00

Abstract: An online connection server is configured to more accurately predict connections for a viewing member of an online connection network. The online connection server may implement a machine-learning model that uses prior interactions by the viewing member to determine those connections that are likely to lead to more substantial interactions with the viewing member. The machine-learning model may be implemented using a reinforcement learning technique, such as a Deep Q network. The online connection server may further implement a state representation module that generates a state from a graph-based embedding of the viewing member profile, where the state is used to train the machine-learning model and determine an optimal candidate to recommend as a connection for the viewing member.

公开(公告)号：US20200213201A1

公开(公告)日：2020-07-02

申请号：US16232357

申请日：2018-12-26

Applicant: Microsoft Technology Licensing, LLC

Inventor： Divya Venugopalan ,  Yiou Xiao ,  Lingjie Weng ,  Heloise Logan ,  Aastha Jain ,  Mahdi Shafiei

IPC: H04L12/24 ,  H04L29/08 ,  H04L12/26

Abstract: In an embodiment, the disclosed technologies include computing a score for a node pair including first and second nodes of a digital connection graph; where nodes of the digital connection graph represent members of an online system; where the online system uses the digital connection graph to determine a runtime decision related to a member represented by the first node; where the score indicates a predicted likelihood of interaction, during a time interval, after a digital connection between the first and second nodes of the node pair; where the predicted likelihood of interaction is determined by comparing a set of statistics computed for the node pair to a digital model; where the digital model has been created using data extracted from post-connection interactions in the online system between members whose nodes are connected in the digital connection graph; causing the score to modify the runtime decision.

公开(公告)号：US20190385069A1

公开(公告)日：2019-12-19

申请号：US16007342

申请日：2018-06-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lingjie Weng ,  Aastha Jain ,  Hema Raghavan ,  Mengda Yang ,  Hongyi Zhang ,  Hari Shankar Sreekumar Menon ,  Shubham Gupta ,  Parinkumar D. Shah

IPC: G06N5/04 ,  G06Q50/00 ,  G06F17/30 ,  G06N99/00

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system retrieves, from a nearline data store, one or more updates representing recent activity for a member of an online network. Next, the system performs one or more queries using data in the updates to identify a set of candidates for recommending to the member. The system then applies one or more machine learning models to features for the set of candidates to generate a ranking of the set of candidates and updates the ranking based on additional features for an additional set of candidates from an offline data store. Finally, the system outputs, to the member, at least a portion of the updated ranking as connection recommendations in the online network.

公开(公告)号：US11941057B2

公开(公告)日：2024-03-26

申请号：US17830067

申请日：2022-06-01

Applicant: Microsoft Technology Licensing, LLC

Inventor： Zhanglong Liu ,  Ankan Saha ,  Yiou Xiao ,  Kathryn L. Evans ,  Aastha Jain ,  Aastha Nigam

IPC: G06F16/901 ,  G06F16/28 ,  G06N3/044 ,  G06N3/08

CPC classification number: G06F16/9024 ,  G06F16/288 ,  G06N3/044 ,  G06N3/08

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

公开(公告)号：US11769048B2

公开(公告)日：2023-09-26

申请号：US17021779

申请日：2020-09-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Parag Agrawal ,  Ankan Saha ,  Yafei Wang ,  Yan Wang ,  Eric Lawrence ,  Ashwin Narasimha Murthy ,  Aastha Nigam ,  Bohong Zhao ,  Albert Lingfeng Cui ,  David Sung ,  Aastha Jain ,  Abdulla Mohammad Al-Qawasmeh

IPC: G06N3/08 ,  G06N3/04 ,  G06F18/214

CPC classification number: G06N3/08 ,  G06F18/2148 ,  G06N3/04

Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

公开(公告)号：US11620595B2

公开(公告)日：2023-04-04

申请号：US16743486

申请日：2020-01-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Siyuan Gao ,  Yiou Xiao ,  Parag Agrawal ,  Aastha Jain

IPC: G06Q10/06 ,  G06N20/00 ,  G06Q50/00 ,  H04L67/50 ,  G06Q10/0631

Abstract: An online connection server is configured to more accurately predict connections for a viewing member of an online connection network. The online connection server may implement a machine-learning model that uses prior interactions by the viewing member to determine those connections that are likely to lead to more substantial interactions with the viewing member. The machine-learning model may be implemented using a reinforcement learning technique, such as a Deep Q network. The online connection server may further implement a state representation module that generates a state from a graph-based embedding of the viewing member profile, where the state is used to train the machine-learning model and determine an optimal candidate to recommend as a connection for the viewing member.

公开(公告)号：US20210034635A1

公开(公告)日：2021-02-04

申请号：US16528060

申请日：2019-07-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Parag Agrawal ,  Aastha Jain ,  Yafei Wang ,  Ashwin Narasimha Murthy

IPC: G06F16/2457 ,  G06F16/248 ,  G06N20/00

Abstract: Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

公开(公告)号：US10481750B2

公开(公告)日：2019-11-19

申请号：US14997384

申请日：2016-01-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Aastha Jain ,  Gloria Lau ,  Vitaly Gordon ,  Jason Schissel

IPC: G06F3/0481 ,  H04L29/06 ,  G06F16/435 ,  G06Q50/00

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, profile edit task information associated with a member of an online social network service is accessed, the profile edit task information identifying one or more candidate profile edit tasks to be performed to update a member profile page of the member. Thereafter, if it is determined that the member recently completed a difficult profile edit task, a difficult candidate profile edit task is identified, and the member is prompted to perform the difficult candidate profile edit task. If it is determined that the member has not recently completed a difficult profile edit task, an easy candidate profile edit task is identified, and the member is prompted to perform the easy candidate profile edit task.

公开(公告)号：US11620512B2

公开(公告)日：2023-04-04

申请号：US16588885

申请日：2019-09-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ashish Jain ,  Smriti R. Ramakrishnan ,  Parag Agrawal ,  Aastha Jain

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/00 ,  G06F16/248

Abstract: Techniques for using machine learning to leverage deep segment embeddings are provided. In one technique, a set of training data is processed using one or more machine learning techniques to train a neural network and learn an embedding for each segment of multiple segments. In response to receiving a request, multiple elements are identified, such as a source entity that is associated with the request, a source embedding for the source entity, a particular segment with which the source entity is associated, a segment embedding for the particular segment, and multiple target entities. For each target entity, a target embedding is identified and the target embedding, the source embedding, and the segment embedding are input into the neural network to generate output that is associated with the target entity. Based on the output, data about a subset of the target entities is presented on a computing device.