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.

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.).