This project is a PyTorch implementation of Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking (PopCon), which is published in PAKDD 2023.
The overview of PopCon is as follows.
PopCon consists of two phases, model training phase and reranking phase.
In the training phase, PopCon trains a bundle recommendation model such as DAM or CrossCBR as a backbone while mitigating its popularity bias by a popularity-based negative sampling.
In the raranking phase, PopCon selects candidate bundles for each user and reranks the candidates by a configuration-aware reranking algorithm to maximize both accuracy and aggregate diversity.
For more details, please refer to our paper.
Our implementation is based on Python 3.8 and Pytorch 1.8.1. Please see the full list of packages required to our codes in requirements.txt
.
We use 3 datasets in our work: Steam, Youshu, and NetEase.
We include the preprocessed datasets in the repository: data/{data_name}
.
We provide DAM, one of the state-of-the-art bundle recommendation models, as a backbone.
It is defined in models.py
.
CrossCBR, another state-of-the-art model, is available at https://github.com/mysbupt/CrossCBR
You can run the pretraining code by python pretrain.py
with arguments --epochs
and --alpha
.
You can also run the reranking code by python reranking.py
with arguments --beta
and --n
.
To run reranking.py
, running pretrain.py
must precede because it returns a recommendation results of a model.
We provide demo.sh
, which reproduces the experiments of our work.
Please cite this paper when you use our code.
@inproceedings{conf/pakdd/JeonKLLK23,
author = {Hyunsik Jeon and
Jongjin Kim and
Jaeri Lee and
Jong-eun Lee and
U Kang},
title = {Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking},
booktitle = {PAKDD},
year = {2023},
}
This software may be used only for non-commercial purposes (e.g., research evaluation) in universities. Please contact Prof. U Kang (ukang@snu.ac.kr) if you want to use it for other purposes or use it in places other than universities.