- automatically published
Client Pull Model for Embedded Product Promotion
User-Side Components
Local Knowledge Base
- Each user device maintains a secure, Hardware and Edge local knowledge base.
- This base contains user preferences, interests, and demographic data, organised as a lookup table. Hashes represent product classes or categories of product that are interesting to the user (opt in)
Nostr Integration
- User’s device includes a Nostr protocol client to interact with the decentralised Nostr network.
- The Nostr client accesses the local knowledge base to retrieve relevant product class hashes.
- These hashes are used to pull personalised marketing content from the Nostr network.
Embedding in User-Side Applications
- Personalised marketing content is seamlessly embedded into the user’s preferred applications, such as Roblox, NVIDIA Omniverse , and web browsers. This ensures relevant and engaging marketing content within the context of the user’s usual digital experiences.
Marketer-Side Components
- Multimodal Product Representation
- Marketers create rich, multi-modal representations of their products, capturing visual appearance, textual descriptions, and other relevant attributes. These are Training and fine tuning using AI to generate variations catering to different user preferences and demographics.
Cloud-Based Latent Space
- Fine-tuned product variations are stored in a cloud-based latent space , a high-dimensional vector space where each point represents a specific product variation.
- This latent space is organised and indexed for efficient retrieval based on user preferences.
Nostr Network Distribution and Support
- Marketers distribute product variations across a cloud of Nostr servers, each variation associated with a unique Nostr event containing metadata and content.
- The Nostr servers act as a decentralised storage and distribution network for marketing content.
- Advertisers and brand leaders support the Nostr network by subsidising network nodes, helping maintain network infrastructure and incentivising node operators.
Interaction Flow
- The user’s device, with a Nostr client, accesses the local knowledge base to retrieve relevant product class hashes.
- These hashes are used to pull personalised marketing content from the Nostr network, which matches hashes with corresponding product variations in the cloud-based latent space.
- The matched product variations are then returned to the user’s device via the Nostr network, ensuring the marketer has no direct access to the user’s personal information or identity.
Benefits and Considerations
User Privacy
- The user’s knowledge base is kept local to their device, using hashes to retrieve personalised content, which enhances Politics, Law, Privacy by avoiding centralized data collection and tracking.
- Hyper personalisation and Dynamic Creative Optimisation (DCO)
- The system delivers content optimised for the user’s language, environment, age, and other demographic factors using AI-powered multi-modal product representations.
- DCO techniques dynamically adapt and optimise creative elements in real-time based on user interactions and preferences.
Scalability and Efficiency
- The Decentralised Web Nostr architecture allows for efficient distribution and retrieval of marketing content.
- Advertiser subsidies help maintain a robust and reliable network infrastructure.
Integration and User Experience
- Personalised marketing content is embedded into the user’s preferred applications for a seamless experience. Ethical Considerations
- It’s crucial to ensure user awareness and consent for using the local knowledge base for personalised marketing.
- Implement clear communication and opt-in mechanisms for transparency and user control.
Measurement and Analytics
- The exploration of privacy-preserving measurement techniques allows for aggregate insights without compromising individual user privacy.
Ecosystem Sustainability
- Advertiser subsidies contribute to the long-term sustainability and growth of the Nostr network, fostering a mutually beneficial ecosystem.
Future Vision
- The system aims to expand advertiser participation and subsidies to strengthen the Nostr network infrastructure further.
- Collaboration with the Nostr community and stakeholders will refine the system’s design and drive adoption.
- Advanced AI and ML techniques will enhance Hyper personalisation and DCO capabilities, fostering a thriving ecosystem benefiting from a privacy-focused approach. -
AI Scientist Paper
-
Here are the three files adapted to your inquiry on client-side hyper-personalization, dynamic creative optimization (DCO), and dynamic content optimization using the Nostr relay protocol, embeddings, and local AI.
ideas.json
[
{
"Name": "local_ai_personalization",
"Title": "Client-Side AI for Hyper-Personalization: Enhancing User Experience While Preserving Privacy",
"Experiment": "Develop a client-side AI system that uses local embeddings to personalize content based on user preferences and interactions. The system will generate personalized multimedia assets in real-time, using local data while maintaining privacy by not sharing any data with external servers. Evaluate the system's performance in terms of user engagement, content relevance, and privacy preservation.",
"Interestingness": 8,
"Feasibility": 7,
"Novelty": 8,
"novel": true
},
{
"Name": "nostr_dynamic_content_optimization",
"Title": "Dynamic Content Optimization Using Nostr Relay Protocol: A Decentralized Approach",
"Experiment": "Implement a dynamic content optimization system that leverages the Nostr relay protocol for real-time content delivery. The system will match content from a distributed network of vendors to users based on locally generated embeddings. Test the system's effectiveness in delivering relevant content while preserving user data sovereignty and minimizing latency.",
"Interestingness": 9,
"Feasibility": 7,
"Novelty": 8,
"novel": true
},
{
"Name": "privacy_preserving_dco",
"Title": "Privacy-Preserving Dynamic Creative Optimization: Leveraging Local AI and Heuristic Matching",
"Experiment": "Design a DCO system that operates entirely on the client side, using heuristic matching to personalize marketing content. The system will use local AI to generate and optimize creative assets without sending any data to external servers. Assess the system's ability to balance personalization and privacy, and compare its performance with traditional server-based DCO systems.",
"Interestingness": 9,
"Feasibility": 8,
"Novelty": 9,
"novel": true
},
{
"Name": "vendor_embedding_optimization",
"Title": "Optimizing Vendor Embeddings for Multimedia Content Personalization",
"Experiment": "Develop a system that creates and optimizes vendor embeddings to personalize multimedia content for users. The system will use local AI to match user preferences with vendor content, ensuring high relevance while preserving privacy. Evaluate the quality of the personalized content and the effectiveness of the embedding optimization.",
"Interestingness": 8,
"Feasibility": 7,
"Novelty": 8,
"novel": true
},
{
"Name": "multimodal_asset_generation",
"Title": "Multimodal Asset Generation Using Local AI and Nostr Protocol",
"Experiment": "Create a system that generates personalized multimodal assets (e.g., text, images, videos) using local AI models. The system will use the Nostr relay protocol to pull relevant content from vendors and integrate it into the user's local environment. Test the system's ability to deliver high-quality personalized content without compromising user privacy.",
"Interestingness": 9,
"Feasibility": 7,
"Novelty": 8,
"novel": true
}
]prompt.json
{
"system": "You are an innovative AI researcher focused on exploring the intersection of privacy, personalization, and decentralized content delivery.",
"task_description": "You are provided with the following file to work with, which explores various approaches to client-side hyper-personalization, dynamic creative optimization, and dynamic content optimization using the Nostr relay protocol, embeddings, and local AI. Your task is to develop a series of small-scale experiments to investigate the potential and challenges of these approaches."
}seed_ideas.json
[
{
"Name": "local_ai_personalization",
"Title": "Client-Side AI for Hyper-Personalization: Enhancing User Experience While Preserving Privacy",
"Experiment": "Develop a client-side AI system that uses local embeddings to personalize content based on user preferences and interactions. The system will generate personalized multimedia assets in real-time, using local data while maintaining privacy by not sharing any data with external servers. Evaluate the system's performance in terms of user engagement, content relevance, and privacy preservation.",
"Interestingness": 8,
"Feasibility": 7,
"Novelty": 8
},
{
"Name": "nostr_dynamic_content_optimization",
"Title": "Dynamic Content Optimization Using Nostr Relay Protocol: A Decentralized Approach",
"Experiment": "Implement a dynamic content optimization system that leverages the Nostr relay protocol for real-time content delivery. The system will match content from a distributed network of vendors to users based on locally generated embeddings. Test the system's effectiveness in delivering relevant content while preserving user data sovereignty and minimizing latency.",
"Interestingness": 9,
"Feasibility": 7,
"Novelty": 8
}
]experiment.py
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
from transformers import FlorenceForImageClassification, FlorenceProcessor
import torch.nn.functional as F
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Data handling classes and functions
class ProductContentDataset(Dataset):
def __init__(self, image_paths, descriptions, generated_contents, transform=None):
self.image_paths = image_paths
self.descriptions = descriptions
self.generated_contents = generated_contents
self.transform = transform
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
image = Image.open(self.image_paths[idx]).convert("RGB")
description = self.descriptions[idx]
generated_content = self.generated_contents[idx]
if self.transform:
image = self.transform(image)
return image, description, generated_content
# Define image transformation pipeline
transform = transforms.Compose([
transforms.Resize((384, 384)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Example data (paths to images, corresponding descriptions, and generated content)
image_paths = ["path/to/product_image1.jpg", "path/to/product_image2.jpg"]
descriptions = [
"This is a high-quality, eco-friendly leather wallet with multiple compartments.",
"Elegant, durable, and perfect for everyday use, this leather bag features modern design."
]
generated_contents = [
"Check out this wallet made from eco-friendly leather, featuring multiple slots.",
"Modern and durable, this leather bag is ideal for daily use with a sleek design."
]
# Initialize dataset and dataloader
dataset = ProductContentDataset(image_paths, descriptions, generated_contents, transform=transform)
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
# Load the Florence2 model and processor
model = FlorenceForImageClassification.from_pretrained("microsoft/florence-base-384")
processor = FlorenceProcessor.from_pretrained("microsoft/florence-base-384")
# Function to calculate image similarity using Florence2 model
def calculate_image_similarity(image):
with torch.no_grad():
output = model(image)
return output
# Function to calculate text similarity
def heuristic_text_match(product_description, generated_content):
vectorizer = TfidfVectorizer().fit_transform([product_description, generated_content])
vectors = vectorizer.toarray()
similarity = cosine_similarity(vectors)
return similarity[0, 1]
# Experiment loop
for batch in dataloader:
images, descriptions, generated_contents = batch
# Forward pass for image similarity
image_similarity_scores = []
for image in images:
image_similarity = calculate_image_similarity(image)
image_similarity_scores.append(image_similarity)
# Calculate text similarity
text_similarity_scores = []
for description, generated_content in zip(descriptions, generated_contents):
text_similarity = heuristic_text_match(description, generated_content)
text_similarity_scores.append(text_similarity)
# Combine image and text similarity
for image_similarity, text_similarity in zip(image_similarity_scores, text_similarity_scores):
overall_similarity_score = (0.6 * image_similarity) + (0.4 * text_similarity)
print(f"Overall Similarity Score: {overall_similarity_score:.4f}")
if overall_similarity_score > 0.75:
print("The consumer-generated content closely matches the product source material.")
else:
print("The consumer-generated content does not sufficiently match the product source material.")
- plot.py