🎵 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.

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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.

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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 command line interface:

python -m src.cli

Running Tests

Run the starter tests with:

pytest

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

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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

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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.

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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

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7. model_card_template.md

Combines reflection and model card framing from the Module 3 guidance. :contentReference[oaicite:2]{index=2}

# 🎧 Model Card - Music Recommender Simulation

## 1. Model Name

Give your recommender a name, for example:

> VibeFinder 1.0

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## 2. Intended Use

- What is this system trying to do
- Who is it for

Example:

> This model suggests 3 to 5 songs from a small catalog based on a user's preferred genre, mood, and energy level. It is for classroom exploration only, not for real users.

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## 3. How It Works (Short Explanation)

Describe your scoring logic in plain language.

- What features of each song does it consider
- What information about the user does it use
- How does it turn those into a number

Try to avoid code in this section, treat it like an explanation to a non programmer.

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## 4. Data

Describe your dataset.

- How many songs are in `data/songs.csv`
- Did you add or remove any songs
- What kinds of genres or moods are represented
- Whose taste does this data mostly reflect

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## 5. Strengths

Where does your recommender work well

You can think about:
- Situations where the top results "felt right"
- Particular user profiles it served well
- Simplicity or transparency benefits

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## 6. Limitations and Bias

Where does your recommender struggle

Some prompts:
- Does it ignore some genres or moods
- Does it treat all users as if they have the same taste shape
- Is it biased toward high energy or one genre by default
- How could this be unfair if used in a real product

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## 7. Evaluation

How did you check your system

Examples:
- You tried multiple user profiles and wrote down whether the results matched your expectations
- You compared your simulation to what a real app like Spotify or YouTube tends to recommend
- You wrote tests for your scoring logic

You do not need a numeric metric, but if you used one, explain what it measures.

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## 8. Future Work

If you had more time, how would you improve this recommender

Examples:

- Add support for multiple users and "group vibe" recommendations
- Balance diversity of songs instead of always picking the closest match
- Use more features, like tempo ranges or lyric themes

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## 9. Personal Reflection

A few sentences about what you learned:

- What surprised you about how your system behaved
- How did building this change how you think about real music recommenders
- Where do you think human judgment still matters, even if the model seems "smart"