- This is the author code of "Bootstrapping User and Item Representations for One-Class Collaborative Filtering" (SIGIR 2021).
- We provide two types of user/item encoder: 1)
BUIR-id- a single embedding layer, and 2)BUIR-nb- the LightGCN.
Our BUIR framework optimizes the representations of positively-related users and items (i.e., users and their highly preferred items) similar to each other, without explicitly using negative user-item interactions.
- python
- pytorch
python main.py --dataset toy-dataset --model buir-id --gpu <gpu-device-idx>
python main.py --dataset toy-dataset --model buir-nb --gpu <gpu-device-idx>
@inproceedings{lee2021bootstrapping,
title = {Bootstrapping User and Item Representations for One-Class Collaborative Filtering},
author = {Lee, Dongha and Kang, SeongKu and Ju, Hyunjun and Park, Chanyoung and Yu, Hwanjo},
booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year = {2021},
pages = {317–326}
}