Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.
Download datasets from RecSysDatasets or their Google Drive. And put the files in ./dataset/ like the following.
$ tree
.
├── Amazon_Beauty
│ ├── Amazon_Beauty.inter
│ └── Amazon_Beauty.item
├── Amazon_Clothing_Shoes_and_Jewelry
│ ├── Amazon_Clothing_Shoes_and_Jewelry.inter
│ └── Amazon_Clothing_Shoes_and_Jewelry.item
├── Amazon_Sports_and_Outdoors
│ ├── Amazon_Sports_and_Outdoors.inter
│ └── Amazon_Sports_and_Outdoors.item
├── ml-1m
│ ├── ml-1m.inter
│ ├── ml-1m.item
│ ├── ml-1m.user
│ └── README.md
└── yelp
├── README.md
├── yelp.inter
├── yelp.item
└── yelp.user
Run duorec.sh.
If you find this repo useful, please cite
@article{DuoRec,
author = {Ruihong Qiu and
Zi Huang and
Hongzhi Yin and
Zijian Wang},
title = {Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation},
journal = {CoRR},
volume = {abs/2110.05730},
year = {2021},
}
We have also implemented CL4SRec, Contrastive Learning for Sequential Recommendation. Change the --model="DuoRec" into --model="CL4SRec" in the duorec.sh file to run CL4SRec.
Our another sequential recommender model MMInfoRec, Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation at ICDM 2021 is also available on GitHub, MMInfoRec.
This repo is based on RecBole.