YouTube AI-Based Recommendation Prototype
Budget: €8 – €30 EUR
Project Title: YouTube Transcript-Based Video Recommendation System
Project Description:
The aim of this project is to develop a prototype for a system that leverages machine learning to recommend YouTube videos based on user queries. This prototype will not include architectural complexities or login functionalities.
Core Features:
Data Storage: The system will utilize in-memory storage to keep a collection of transcriptions from YouTube videos. Initially, the prototype will include transcriptions from five specific videos.
Search Functionality: Users can enter queries into a search bar to find videos relevant to their interests. Upon receiving a query, the system will analyze the stored video transcriptions using a GPT model or a similar deep learning framework to identify the most relevant video suggestions.
Video Embedding: The actual videos will be embedded directly from YouTube, ensuring users can watch the recommended videos within the interface.
Technologies Used:
C#
.NET 9
Blazor for client-side web development
This prototype is designed to showcase the potential of integrating advanced AI text analysis with video content platforms to enhance user experience through tailored video recommendations.
Project Description:
The aim of this project is to develop a prototype for a system that leverages machine learning to recommend YouTube videos based on user queries. This prototype will not include architectural complexities or login functionalities.
Core Features:
Data Storage: The system will utilize in-memory storage to keep a collection of transcriptions from YouTube videos. Initially, the prototype will include transcriptions from five specific videos.
Search Functionality: Users can enter queries into a search bar to find videos relevant to their interests. Upon receiving a query, the system will analyze the stored video transcriptions using a GPT model or a similar deep learning framework to identify the most relevant video suggestions.
Video Embedding: The actual videos will be embedded directly from YouTube, ensuring users can watch the recommended videos within the interface.
Technologies Used:
C#
.NET 9
Blazor for client-side web development
This prototype is designed to showcase the potential of integrating advanced AI text analysis with video content platforms to enhance user experience through tailored video recommendations.