Recommendation System: Content-Based Filtering

Welcome to the AI Recommendation System project! This repository contains the code and resources needed to run the recommendation system, demonstrated on my YouTube video.

Project Overview

This project is a content-based recommendation system built using Python. It consists of the following components:

• Data Insertion: Script to insert embeddings into the Qdrant vector store database (located in src/insert_collection_qdrant.py)
• Utilities: Functions used to build the recommender system (located in src/utils.py)
• Frontend: Developed using Streamlit (located in src/app.py)
• Dataset: The dataset used for the system (located in the data folder)
• Requirements: List of required libraries (located in requirements.txt)
• Environment Variables: Example file for necessary credentials (located in .env.example)

Getting Started

Installation

1. Clone the repository:

   git clone https://github.com/Eduardovasquezn/movie-recommender.git

2. Navigate to the project directory:

   cd movie-recommender

3. Create and activate virtual environment:

   python -m venv venv
   venv/Scripts/activate

4. Install the required libraries:

   pip install -r requirements.txt

Running the Application

1. Set up the environment variables. Create a .env file using .env-example as a template:

   cp .env-example .env

2. Insert embeddings into the Qdrant database:

   python src/insert_collection_qdrant.py

3. Start the Streamlit app:

   streamlit run src/app.py

Learn More

Don't forget to check out the video, like, comment, and subscribe for more advanced tutorials!

If you found the content helpful, consider subscribing to my YouTube channel to support me.