Rekommend

Rekommend is a personalized music recommendation system designed to help users discover new songs based on their preferences. The system uses an autoencoder neural network to encode and analyze song features, providing accurate song recommendations. This README will guide you through the project’s setup, usage, and architecture.

Features

Personalized Recommendations: Provides song suggestions based on a selected song’s features.
Search Functionality: Allows users to search for songs by name and choose from a list of results.
Colorful Command-Line Interface: Engages users with a visually appealing and intuitive text interface.

Setup

Prerequisites

Python 3.x
Required Python packages (listed in requirements.txt)

Installation

Clone the Repository

git clone https://github.com/yourusername/rekommend.git
cd rekommend

2. Install Dependencies

Create a virtual environment and install the required packages:

```bash
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

3. Download Song Data

Ensure you have the song_data.csv file in the project directory. This file should contain song information including: Check out Moosic-Rekom dataset

Name
Artist
Danceability
Energy
Keywords

Usage

1. Run the Application

Execute the main script to start the application:

```bash
python main.py

2. Interact with the System

Search for Songs: Enter a song name when prompted. The system will display a list of matching songs.
Select a Song: Choose a song by entering the corresponding number. The system will provide recommendations based on your selection.
Exit: Type ‘exit’ to quit the application.

Architecture

The Rekommend system consists of the following components:

Data Processing:
- data_processing.py: Handles loading and preprocessing of song data. Converts raw data into a format suitable for model training and recommendation.
Model:
- model.py: Defines the autoencoder neural network. Includes functions to build, train, encode features, and save/load the model.
Recommendation:
- recommendation.py: Contains functions to recommend songs based on a user’s choice. Uses encoded features to find similar songs.
Utilities:
- utils.py: Provides helper functions for interacting with the song dataset, such as searching songs by name.
Main Application:
- main.py: The main script that integrates all components. Provides a command-line interface (CUI) for users to interact with the system.

Pradevel/Rekommend