Recommending edges via importance aware machine learned model

US11769048B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Parag Agrawal · Mountain View, US
• Ankan Saha · San Francisco, US
• Yafei Wang · Shanghai, CN
• Yan Wang · Taichung, TW
• Eric M. Lawrence · Redmond, US
• Ashwin Murthy · Bellevue, US
• Aastha Nigam · Sunnyvale, US
• Bohong Zhao · San Jose, US
• Albert Lingfeng Cui · Palo Alto, US
• David Sung · Sunnyvale, US
• Aastha Jain · Sunnyvale, US
• Abdulla Mohammad Al-Qawasmeh · San Francisco, US

Key dates

• Filing date: Sep 15, 2020
• Grant date: Sep 26, 2023
• Expiry date: Nov 26, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N5/02
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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.