Snap inc. (20240202550). MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS simplified abstract
Contents
MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS
Organization Name
Inventor(s)
John Cain Blackwood of Los Angeles CA (US)
Jason Brewer of Mountain View CA (US)
Nima Khajehnouri of Santa Monica CA (US)
Hadi Minooei of Irvine CA (US)
Benjamin C. Steele of Oak Park CA (US)
Qian You of Marina del Rey CA (US)
MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202550 titled 'MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS
The patent application describes systems and methods for generating lookalike data for users based on seed data and user data, capturing social graph data, computing social graph features, training a lookalike model, and generating lookalike scores for users.
- Receiving a request for lookalike data with seed data and generating sample data for users.
- Capturing social graph data and computing social graph features for users.
- Training a lookalike model based on sample data and social graph features.
- Generating lookalike scores for users using the trained model.
- Creating a list with unique identifiers and associated lookalike scores for users.
Potential Applications: - Targeted advertising - Personalized recommendations - Market segmentation analysis
Problems Solved: - Improving targeted marketing efforts - Enhancing user engagement - Optimizing advertising spend
Benefits: - Increased conversion rates - Enhanced user experience - Improved ROI on marketing campaigns
Commercial Applications: Title: "Enhancing Targeted Marketing Strategies with Lookalike Data Analysis" This technology can be used by digital marketing agencies, e-commerce platforms, and social media companies to improve their advertising and user targeting strategies.
Prior Art: Readers can explore prior art related to lookalike modeling, social graph analysis, and targeted advertising technologies to understand the evolution of these concepts.
Frequently Updated Research: Stay informed about the latest advancements in lookalike modeling, social graph analysis, and data-driven marketing strategies to leverage cutting-edge technologies for business growth.
Questions about Lookalike Data Analysis: 1. How does lookalike modeling differ from traditional user segmentation techniques? 2. What are the key factors influencing the accuracy of lookalike scores in this system?
Original Abstract Submitted
systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. the systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.
