GEA Publishing: A New Advertising Format

From 12 to 31 October 2026, ASUS launched an innovative advertising campaign relying on Reworld MediaConnect’s ad-serving service and the agency Heroiks. This initiative aims to promote its new Zenbook 14 laptop using a novel advertising format called “GEA Publishing”.

GEA Publishing (Gestion d’Experiences Algorithmiques Publishing) is a new approach that inserts product recommendations into conversational artificial intelligence engines. This format stands out through its use of the authority of the specialist media site Les Numériques, thereby enhancing the credibility and effectiveness of the campaign.

Software Architectures and Frameworks

The implementation of GEA Publishing relies on a robust software stack. The main technologies used include:

  • Python : Used for product recommendation analysis and personalization.
  • TensorFlow: machine learning framework used for optimizing recommendation algorithm algorithms.
  • React.js : For front-end development of integrating recommendations into chatbot interfaces.

Frameworks

  • Flask : Used for creating simple and effective APIs.
  • Django: framework for managing complex data and back-ends.

Material Infrastructure

To ensure the performance and reliability of the campaign, ASUS has put in place a robust hardware infrastructure:

  • Cloud Servers: Used for backend infrastructure, providing high availability and scalability flexibility.
  • Virtual Machines: For executing front-end services and managing resources.
  • Storage Networks: To ensure data reliability and transfer speed.

Technical Implementation

The collaboration between ASUS, Reworld MediaConnect, and the Heroiks agency has enabled a smooth and effective integration of product recommendations into conversational AI engines. This was achieved through a series of well-defined protocols and APIs that facilitate communication between the various systems.

Protocols and APIs

  • REST APIs : For sharing data between different services.
  • WebSockets: For real-time communication between clients and servers.

Example Code

Here is a simplified example of an AI conversational recommendation code:

python 
# Example of Python code for recommendation
def recommend_product(user_data):
    # Load user data
    user_profile = load_user_profile(user_data)
    
    # Use TensorFlow for recommendation
    product_recommendations = tensorflow_recommendation(user_profile)
    
    # Return top 3 recommendations
    return product_recommendations[:3]

Cet article est rédigé par Blaise Grenier, Journaliste reporter d'images et auteur. Découvrir le parcours de l'auteur et la charte éditoriale »