Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
The code for the paper titled 'Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning' is abbreviated as DWHRec.
The project is organized into two principal directories: src and datasets.
The src directory houses the implementation of the algorithms, whereas the datasets directory encompasses the datasets employed for testing.
The file main.py, situated at the same directory level as src and datasets, functions as the entry point for the program.
main.py contains some parameters.
| Parameters | Meanings | Default value |
|---|---|---|
| --dataset | The specified name indicates the dataset to be loaded. | 100k |
| --r | The variable |
5 |
| --k | The variable |
100 |
| --s | The symbol |
50 |
| --w | The parameter |
5 |
Execute the subsequent command within any terminal or terminal-emulating application.
For example:
(1). A straightforward approach to utilizing the system is as follows:
python main.py
In this instance, all parameters have been assigned their default values.
(2). If you desire to assign custom values to each parameter, please follow the procedure below.
python main.py --dataset 100k --r 5 --k 200 --s 200 --w 5