Recommendations using session relevance and incremental learning

US12223554B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Rohan Ramanath · Saratoga, US
• Konstantin Salomatin · San Francisco, US
• Jeffrey Gee · South San Francisco, US
• Onkar Anant Dalal · Santa Clara, US
• Gungor Polatkan · San Jose, US
• Sara Smoot Gerrard · Stanford, US
• Deepak Kumar · Sunnyvale, US
• Rupesh Gupta · Sunnyvale, US
• Jiaqi Ge · Sunnyvale, US
• Lingjie Weng · Sunnyvale, US
• Shipeng Yu · Sunnyvale, US

Key dates

• Filing date: Jun 25, 2020
• Grant date: Feb 11, 2025
• Expiry date: Feb 22, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/08
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.