Social Authenticity Network
  • Introduction
    • What is #SocAuth?
    • AN Social Authenticity Network
    • Vision & Mission
  • PROBLEM AND SOLUTION
    • Problem Statement
    • Solution Statement
  • Markets and Competitors Analysis
    • Markets & Growth
    • AN vs Competitors
  • AN Social Authenticity Solutions
    • AN Neural Network
      • Network Services
      • Network Scaling
      • Network Clusters
    • AN AI Powerhouse
      • Interactions
      • Network Embedding
      • AI Modular Infrastructure
      • Dimensional Analysis
      • Blau Spaces
      • DeepNet
    • Use Cases
  • AN Social Authenticity Network
    • User Data Sovereignty
    • Community Validation
    • Genuine Reviews
    • Social Trust Metric (STM)
      • STM Features
    • AN ID/DID
      • ID/DID Solutions
  • AN SocAuth Applications
    • AN Mobile App
      • AN Social Feeds
      • App Services & Features
    • AN Interactive
      • Wearable & Mobility Devices
  • Tokenomics and Revenues
    • Tokenomics
      • Token Utility
      • User Rewards
      • Buyback & Burn
    • Revenues
  • User Centric Rewards System
    • Rewards System
      • ANr and ANp Points
      • Rewards Rates
      • User Contributions
  • Roadmap
    • 2023 - 2024
  • OUR TEAM
    • Team Info
  • AANN.ai Lab
    • Research
    • Development
  • Authentica Foundation
    • About Authentica
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AI Modular Infrastructure

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Last updated 1 year ago

In-Network Timeline Indexer

Timeline: Refers to the chronological order of posts, usually displayed on a user's profile in a social media platform and In-Network posts are the messages, pictures, or updates shared by people within a specific social network or platform.

This module generates recommendations of in-network posts for the timeline of the user using the mobile app.

Ex-Network Timeline Content Recommendation Mixer

In the context of social media, "Ex-Network" refers to people or content that is not part of your group of followed users. This module is similar to the In-Network module above but recommends content from users that you do not follow.

AN XX-Like

This Recommender sub-module is tasked with recommending content, users ‘may also like’ based on the ML analysis of their previous actions and actions of users that the machine learning algorithm has determined as being alike in multi-dimensional vector space.

ML Equalizer

This module takes all of the recommendations from the In-Network, Ex-Network, and likes analysis and does one final analysis to generate the most probable posts that elicit user interaction.

Filtering / Heuristics

This module serves as the last stage in the process, considering user preferences and post-filtering settings. It ultimately achieves a delicate equilibrium by incorporating a mix of advertisements into the user's timeline feed.