galafis/Recommendation-System-ML
Content-based movie recommendation using TF-IDF and cosine similarity with scikit-learn
What's novel
Content-based movie recommendation using TF-IDF and cosine similarity with scikit-learn
Code Analysis
2 files read · 2 roundsA content-based movie recommendation system that uses TF-IDF vectorization on movie metadata (genres and plot keywords) to compute cosine similarity and return top-K similar titles.
Strengths
Clean class-based architecture with clear separation of concerns (load, preprocess, train, recommend). The code is well-documented in the README, supports Docker, and handles basic edge cases like missing files or empty datasets gracefully.
Weaknesses
Lacks unit tests entirely. Error handling is minimal (e.g., no case normalization for text features, potential issues with non-English keywords due to hardcoded English stop words). The main block logic overwrites data aggressively if the CSV is missing columns.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
7
Contributors
1
Files
9
Active weeks
4
Repository
Language
Python
Stars
1
Forks
0
License
MIT