Implementation of some algorithms used in Recommender Systems. I implemented three algorithms: Collaborative Filtering, SVD, and CUR Decomposition.
- Make sure Python 3.10+ is installed.
- Install pipenv.
$ pip install pipenv
- Install requirements
$ pipenv install
- Split and process the dataset
$ pipenv run python src/preprocess.py
- Run the algorithms
$ pipenv run python src/recommend.py
MovieLens 1M Dataset has been used. Data files are present in data directory. Data has around 6000 users, 3000 movies and 1 million ratings.
NOTE: I have not tested the code on other datasets, but with minor changes it should work fine. Though, we might face memory issues on very huge datasets.
| Algorithm | RMSE | Precision on top 100 (%) | Spearman Rank Correlation (%) |
|---|---|---|---|
| Collaborative Filtering | 0.005736 | 99.24713 | 99.99999 |
| Collaborative Filtering with Baseline Approach | 0.005937 | 99.12928 | 99.99999 |
| SVD | 0.002870 | 99.04599 | 99.99999 |
| SVD with 90% energy | 0.002867 | 99.01968 | 99.99999 |
| CUR | 0.002943 | 98.47204 | 99.99999 |
| CUR with 90% energy | 0.002944 | 98.46733 | 99.99999 |
Test Size: 25%
Collaborative Filtering Neighbourhood Size: 150
SVD and CUR Concepts: 40
CUR Columns/Rows Selected: 160
NOTE: You can change these parameters in config.py
I did this project to get a better understanding of the said algorithms. In a production system, we should use more efficient implementations, such as those available in scipy or scikit-learn.