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 |
This software may be used only for non-commercial purposes (e.g., research evaluation) in universities. Please contact Prof. U Kang ([email protected]) if you want to use it for other purposes or use it in places other than universities.
@inproceedings{KimJLK23,
author = {Jongjin Kim and
Hyunsik Jeon and
Jaeri Lee and
U Kang},
title = {Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation},
booktitle = {PAKDD},
year = {2023},
}