This project is a pytorch implementation of Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation. This paper proposes a novel approach, Diversely Regularized Matrix Factorization (DivMF), to achieve high aggregate level diversity in recommendation system while maintaining accuracy. This project provides executable source code with adjustable hyperparameters as arguments and preprocessed datasets which used in the paper.
You can run a demo script demo.sh that reproduces the experimental results in the paper.
You can change the hyperparameters by modifying arguments of main.py.
Unpack zip files in data directory to use large datsets: Yelp-15, Gowalla-15, Movielens-10M.
Preprocessed data are included in the data directory.
| Name | Users | Items | Interactions | Download |
|---|---|---|---|---|
| Yelp-15 | 69,853 | 43,671 | 2,807,606 | Link |
| Gowalla-15 | 34,688 | 63,729 | 2,438,708 | Link |
| Epinions-15 | 5,531 | 4,286 | 186,995 | Link |
| Movielens-10M | 69.878 | 10,677 | 10,000,054 | Link |
| Movielens-1M | 6,040 | 3,706 | 1,000,209 | Link |