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：US20200226694A1

公开(公告)日：2020-07-16

申请号：US16248946

申请日：2019-01-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lu Chen ,  Shaunak Chatterjee ,  Ankan Saha

IPC: G06Q50/00 ,  G06F16/951 ,  H04L12/58

Abstract: A computer-implemented method may determine content items regarding a subject to be high demand and sufficient supply, low demand and supply constrained, high demand and supply constrained, or low demand and supply constrained. The computer-implemented method may determine the following: a supply and demand of content items regarding a subject for members, supply demand ratios for the content items regarding the subject for each of the plurality of members, a median supply demand ratio of the supply demand ratios, a total demand for the content items regarding the subject, a median total demand of total demands for the content items regarding subjects for the members, and a median of median supplies demand ratios for the content items regarding the subjects for the members. The method may perform steps to improve demand or supply of a connection network.

公开(公告)号：US20240411573A1

公开(公告)日：2024-12-12

申请号：US18208199

申请日：2023-06-09

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yao CHEN ,  Lingjie Weng ,  Arvind Murali Mohan ,  Hongbo Zhao ,  Lu Chen ,  Dipen Thakkar ,  Xiaoxi Zhao ,  Shifu Wang ,  Jim Chang ,  Daniel D. Thorndyke ,  Smriti R. Ramakrishnan

IPC: G06F9/451

Abstract: In an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. These next actions are called “next best actions.” The machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. The use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. Likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.