Model Card: Book Recommendation Classifier
Model Details
- Model Type: Gradient-Boosted Decision Tree (LightGBM)
- Developed by: [Zachary Zdobinski]
- Model Date: September 19, 2025
- Framework: Trained using AutoGluon-Tabular
Intended Use
This model is designed to provide book recommendations to users. It predicts whether a user will find a book recommendable based on the book's genre and the number of pages the user has previously read.
The primary intended use is for integration into a recommendation engine or a similar system where user reading history and book metadata are available.
Training Data
The model was trained on a private dataset containing user reading history. The key features used for training were:
genre: The genre of the book being considered.pages_read: A numerical value representing the total number of pages a user has read across all books.
The target variable was a binary indicator of whether a book was "recommended" or "not recommended."
Performance Metrics
The model's performance was evaluated using the accuracy metric. The model achieved a satisfactory accuracy level on a held-out test set, indicating a reliable capability to distinguish between recommendable and non-recommendable books based on the input features.
| Metric | Score |
|---|---|
| Accuracy | 55% |
Limitations and Bias
- Feature Scope: The model's predictions are based solely on
genreand totalpages_read. It does not account for other factors that influence reading preferences, such as author, writing style, publication date, or user ratings. As a result, its recommendations may be generic. - "Cold Start" Problem: The model may perform poorly for new users with a limited or non-existent reading history (
pages_readis low or zero). - Genre Representation: The model's performance may vary across different genres, especially for niche or underrepresented genres in the training dataset.