🎵 Music Recommender Simulation

Project Summary

In this project you will build and explain a small music recommender system.

Your goal is to:

• Represent songs and a user "taste profile" as data
• Design a scoring rule that turns that data into recommendations
• Evaluate what your system gets right and wrong
• Reflect on how this mirrors real world AI recommenders

Replace this paragraph with your own summary of what your version does.

---

How The System Works

Explain your design in plain language.

Some prompts to answer:

• What features does each Song use in your system
  • For example: genre, mood, energy, tempo
• What information does your UserProfile store
• How does your Recommender compute a score for each song
• How do you choose which songs to recommend

You can include a simple diagram or bullet list if helpful.

---

Getting Started

Setup

1. Create a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate      # Mac or Linux
   .venv\Scripts\activate         # Windows
   

2. Install dependencies

pip install -r requirements.txt

3. Run the app:

python -m src.main

Running Tests

Run the starter tests with:

pytest

You can add more tests in tests/test_recommender.py.

---

Sample Recommendation Output

Paste a sample of your recommender's output here as a text block so a reader can see what it produces:

# e.g.:
# User profile: genre=indie, mood=chill, energy=low
# Recommendations:
#   1. ...
#   2. ...
#   3. ...

Screenshot or video (optional):

---

Experiments You Tried

Use this section to document the experiments you ran. For example:

• What happened when you changed the weight on genre from 2.0 to 0.5
• What happened when you added tempo or valence to the score
• How did your system behave for different types of users

---

Limitations and Risks

Summarize some limitations of your recommender.

Examples:

• It only works on a tiny catalog
• It does not understand lyrics or language
• It might over favor one genre or mood

You will go deeper on this in your model card.

---

Reflection

Read and complete model_card.md:

Model Card

Write 1 to 2 paragraphs here about what you learned:

• about how recommenders turn data into predictions
• about where bias or unfairness could show up in systems like this