Predicting Average IMDb Movie Ratings Using Text Embeddings of Movie Metadata
19 hours ago
- A Reddit user was rejected from a data science role after submitting a neural network model for a take-home assignment predicting IMDb movie ratings, sparking debate about model interpretability.
- The author explores using LLM embeddings (from Alibaba's gte-modernbert-base) to encode movie metadata, then building predictive models, finding that a small LLM trained from scratch achieved the best test MSE of 1.026.
- Traditional models like SVM (MSE 1.087) and MLP on embeddings (MSE 1.074) performed well, but training a dedicated LLM outperformed them, though overfitting was a challenge.
- The author used Polars for efficient data processing, UMAP for visualization, and GPU acceleration (cuML) for model training, highlighting the practicality of embeddings-based approaches for data science tasks.