sisinflab / Multi-Objective-Personalized-Ranking-for-Recommendation

Multi-Objective Personalized Ranking for Recommendation

This repository contains the source codes and datasets of the paper Multi-objective Personalized Ranking for Recommendation submitted at SIGIR 2024.

Requirements

We implemented and tested the models in Python 3.8.10, with PyTorch==2.0.1 and CUDA 11.7. The LightGCN moodel require PyTorch Geometric. Then, the requirements listed in the req_MPR.txt file refer to these versions. You may create the virtual environment with the requirements file as follows:

$ python3 -m venv venv
$ source venv/bin/activate
$ pip install --upgrade pip
$ pip install -r req_MPR.txt

Data

In the folder data, you can find the data used in our work (Amazon Baby, Facebook Books, MovieLens1M). We provide the split version of the data.

Run the models

In the following, we explain how to run the models within the paper.

• BPRMF, LightGCN, MultiFR, and MPR can be executed through the main.py script. Specifically, you should refer to the configuration files contained into the folder config_files. You may train the model by running the following code:

  $ python3 -u main.py --config [CONFIGURATION_FILE_NAME]
  

• CPFAIR can be executed through the main_cpfair.py script. It requires the matrix of scores (user, item) predicted by a backbone model (in this paper BPRMF and LightGCN). For this reason, this code is prepared in order to load such matrix saved with .npz extension from a folder called arrays. You may train the model by running the following code:

  $ python3 -u main_cpfair.py
  

Evaluation

To evaluate the models, we relied on the public and open-source framework Elliot. Here, given a pre-obtained recommendation list by running the models as explained above, you can compute the metrics discussed within the paper. Due to GitHub size limitations, we load a compressed version of Elliot on this repository (elliot_MPR.zip). Please, refer to Elliot official documentation for further details on how compute the metrics. We add an example of configuration file in the config_files folder within the compressed file.