Notes by StaszekM:

For our assignment, we use only our own data, not any samples proposed by the authors

1. Put file_cutoff.pkl and metadata_with_embeddings.pkl inside ./artifacts
2. Run transform_artifacts.ipynb
3. Rename the resulting CSVs to rekomDataset.inter and rekomDataset.item
4. Put these files to the directory as shown below

$ tree
.
├── rekomDataset
│   ├── rekomDataset.inter
│   └── rekomDataset.item

5. Run your training with:

$ python run_seq.py --dataset='rekomDataset' --train_batch_size=256 --lmd=0.1 --lmd_sem=0.1 --model='CL4SRec' --contrast='us_x' --sim='dot' --tau=1

DuoRec

Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.

Usage

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.

Cite

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},
}

MISC

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.

Credit

This repo is based on RecBole.