Implement CLI-first music recommender simulation - #33
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nahomtesfay212025-cmyk wants to merge 2 commits into
Open
Implement CLI-first music recommender simulation#33nahomtesfay212025-cmyk wants to merge 2 commits into
nahomtesfay212025-cmyk wants to merge 2 commits into
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Add CSV loading, additive scoring (genre/mood/energy/acousticness), and top-k ranking in recommender.py, wire it into main.py with formatted terminal output, and document the design (Algorithm Recipe, data flow diagram, sample output, expected biases) in README.md. Working end-to-end via Loading songs from data/songs.csv... ================================================== Top 5 Recommendations ================================================== 1. Library Rain (Score: 8.00) Because: matches favorite genre 'lofi', matches favorite mood 'chill', energy 0.35 close to target 0.35, acoustic sound matches your preference 2. Midnight Coding (Score: 7.86) Because: matches favorite genre 'lofi', matches favorite mood 'chill', energy 0.42 close to target 0.35, acoustic sound matches your preference 3. Focus Flow (Score: 5.90) Because: matches favorite genre 'lofi', energy 0.40 close to target 0.35, acoustic sound matches your preference 4. Spacewalk Thoughts (Score: 4.86) Because: matches favorite mood 'chill', energy 0.28 close to target 0.35, acoustic sound matches your preference 5. Coffee Shop Stories (Score: 2.96) Because: energy 0.37 close to target 0.35, acoustic sound matches your preference.
Document 6 adversarial user profiles with real terminal output in README.md, apply and verify a genre/energy weight-shift experiment in recommender.py, and fill out all model_card.md sections including Evaluation, Limitations and Bias, and a reflection on the engineering process.
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Summary
Test plan