18293993. ADVERTISEMENT EFFECT PREDICTION DEVICE simplified abstract (NTT DOCOMO, INC.)

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Organization Name

NTT DOCOMO, INC.

Inventor(s)

Takahito Ishii of Chiyoda-ku (JP)

Tsukasa Demizu of Chiyoda-ku (JP)

This abstract first appeared for US patent application 18293993 titled 'ADVERTISEMENT EFFECT PREDICTION DEVICE

The patent application describes an advertising effect prediction device that utilizes machine learning to predict the click-through rate of individual users based on layout information and user attributes.

  • Construction unit converts layout information into a graph structure using a GNN scheme.
  • Machine learning is performed using feature quality of nodes and user attribute information to predict click-through rate.
  • Prediction unit uses the same scheme to predict click-through rate based on target user attribute information and layout information.
  • The device aims to improve advertising effectiveness by predicting user behavior accurately.
  • The innovation lies in the use of graph structures and machine learning for personalized click-through rate prediction.
      1. Potential Applications:

The technology can be applied in digital marketing, online advertising, and e-commerce platforms to optimize ad campaigns and increase user engagement.

      1. Problems Solved:

The device addresses the challenge of accurately predicting user behavior in response to advertising content, leading to more effective marketing strategies.

      1. Benefits:

- Enhanced targeting of advertising campaigns - Improved ROI for advertisers - Personalized user experience - Increased click-through rates

      1. Commercial Applications:

Predictive advertising analytics for digital marketing agencies, e-commerce platforms, social media companies, and online publishers.

      1. Prior Art:

Researchers can explore prior studies on machine learning in advertising, graph neural networks, and personalized marketing strategies.

      1. Frequently Updated Research:

Stay updated on advancements in machine learning algorithms for advertising, user behavior prediction, and personalized marketing technologies.

        1. Questions about Advertising Effect Prediction Device:

1. How does the device utilize user attribute information to predict click-through rates? 2. What are the key advantages of using graph structures in predicting advertising effectiveness?


Original Abstract Submitted

An advertising effect prediction device includes: a construction unit that converts layout information of delivered manuscript into a graph structure through collation with a flow line of a user using a scheme related to a GNN, and performs machine learning using a feature quality of each node in the graph structure and delivery user attribute information as explanatory variables and a flag indicating presence or absence of a click of each delivery user based on delivery result as an objective variable to construct a prediction model for predicting click through rate of an individual user; and a prediction unit that converts the layout information into a graph structure using the same scheme based on target user attribute information and the delivered manuscript and inputs the feature quantity and the target user attribute information to a prediction model, to obtain click through rate prediction value of the individual user.

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