Recommendation Systems with Mixed-Type Data: An Empirical Comparison on MovieLens-1M

This repository contains the implementation accompanying my AIMS Rwanda research thesis, which compares three families of recommender systems on the MovieLens-1M dataset under a unified, leakage-free evaluation protocol. The central question is how different modelling families handle datasets that mix numerical, categorical, and binary features alongside user-item interaction data.

Author: Olusola Timothy Ogundepo

Supervisor: Professor Ernest Fokoué

Institution: African Institute for Mathematical Sciences (AIMS), Rwanda

Models compared

Model	Family	Mixed-type handling
Matrix Factorisation (SGD with biases)	Collaborative filtering baseline	None, ratings only
CatBoost (base, 22 features)	Gradient-boosted regression trees	Native categorical splits
CatBoost (extended, 42 features)	Gradient-boosted regression trees	Adds rating-derived statistics
Feature-augmented LightGCN	Graph neural network	Feature projection at layer 0 + graph propagation

All four variants share the same time-based 80/10/10 split, the same evaluation protocol, and the same encoded feature representation, so any performance difference is attributable to the modelling family rather than the experimental setup.

Mathematical summary

Given user features $z_u$, item features $x_i$ and the global training mean $\mu$, the prediction rules used are:

• Matrix factorisation: $\hat r_{ui} = \mu + b_u + b_i + \mathbf{p}_u \cdot \mathbf{q}_i$
• CatBoost: $\hat r_{ui} = \mu + \sum_{t=1}^{T} \gamma, h_t([z_u, x_i])$
• LightGCN: $\hat r_{ui} = \mu + b_u + b_i + \mathbf{e}_u^{(L)} \cdot \mathbf{e}_i^{(L)}$ with layer-mean aggregation over $L = 2$ propagation rounds and $\mathbf{e}_u^{(0)} = \mathrm{emb}(u) + W_u z_u$.

Evaluation uses RMSE and MAE for rating accuracy, and Precision@10, Recall@10, NDCG@10 for ranking quality.

Repository contents

• RS_Code_Implementation.ipynb — main notebook with the full pipeline: data loading, feature engineering, time-based split, training of all four models, evaluation, and interpretability diagnostics (CatBoost feature importance and PCA of LightGCN item embeddings).
• RS_Experimentation.ipynb — exploratory notebook with side experiments and intermediate checks.
• ml-1m-README.txt — original README from the MovieLens-1M release describing the raw files.

The MovieLens-1M data files are not included in the repository.

Data

MovieLens-1M is a public benchmark dataset distributed by GroupLens.

• Ratings: $1{,}000{,}209$ on the integer scale ${1, \dots, 5}$
• Users: $6{,}040$ with gender, age band, and occupation
• Items: $3{,}706$ films with release year and 18 genre flags
• Period: April 2000 to February 2003

Download the dataset from https://grouplens.org/datasets/movielens/1m/, unzip it, and place the three .dat files inside a datasets/ folder next to the notebooks:

Recommendation-System-using-Mixed-Type-Dataset-Implementation/
├── datasets/
│   ├── ratings.dat
│   ├── users.dat
│   └── movies.dat
├── RS_Code_Implementation.ipynb
└── ...

Setup

The code is written in Python 3. The main dependencies are:

• numpy, pandas, matplotlib, seaborn
• scikit-learn (min-max scaling, PCA, metrics)
• catboost
• torch
• torch-geometric (provides the LGConv layer)

A minimal install:

pip install numpy pandas matplotlib seaborn scikit-learn catboost torch torch-geometric

Random seeds are fixed at 42 for NumPy, PyTorch, and the ranking sampler so that splits, mini-batches, and sampled evaluation users are reproducible.

Running the experiments

Open RS_Code_Implementation.ipynb and run the cells top to bottom. The notebook is organised in eight parts:

1. Setup
2. Data loading and exploration
3. Feature engineering and time-based split
4. Matrix factorisation baseline
5. CatBoost (base and extended)
6. Feature-augmented LightGCN
7. Comparison table and bar plots
8. Interpretability analysis (CatBoost feature importances, LightGCN embedding PCA)

Results on the test split

Model	RMSE ↓	MAE ↓	P@10 ↑	R@10 ↑	NDCG@10 ↑
Matrix factorisation	0.9155	0.7170	0.1230	0.0345	0.1283
CatBoost (base)	1.0195	0.8214	0.0595	0.0146	0.0536
CatBoost (extended)	0.9387	0.7378	0.0675	0.0198	0.0733
Feature-augmented LightGCN	0.9087	0.7175	0.1195	0.0366	0.1350

Bold = best in column. Feature-augmented LightGCN wins on three of the five metrics and is essentially tied with matrix factorisation on the remaining two.

Citing this work

If you use this code, please cite the accompanying thesis (AIMS Rwanda, 2026). A BibTeX entry will be added once the thesis is deposited.

License

The code in this repository is released under the MIT License. The MovieLens-1M dataset is distributed separately under its own terms; see GroupLens for details.