EC4SRec (Updating)

Code for the CIKM2022 Paper "Explanation Guided Contrastive Learning for Sequential Recommendation"

EC4SRec News

09/27/2022: We updated DuoRec on ml-100k.<br/> 09/19/2022: We updated CL4SRec w/ X-Aug (Ours) on ml-100k.<br/> 09/17/2022: We updated SASRec and CL4SRec on ml-100k.

EC4SRec To-Do list

• [x] Update SASRec.
• [x] Update CL4SRec.
• [x] Update CL4SRec w/ X-Aug (Ours).
• [ ] Update CoSeRec.
• [ ] Update CoSeRec w/ X-Aug (Ours).
• [ ] Update ContraRec.
• [ ] Update ContraRec w/ X-Aug (Ours).
• [x] Update DuoRec.
• [x] Update DuoRec w/ X-Aug (Ours).
• [ ] Update ICL.
• [ ] Update EC4SRec (Ours).

Quick Start

Command for CL4SRec

python run_train.py --dataset=ml-100k --model=SASRec --method=CL4SRec --gpu_id=13
python run_train.py --dataset=Amazon_Beauty --model=SASRec --method=CL4SRec --gpu_id=4

Command for CL4SRec w/ X-Aug (Ours)

python run_train.py --dataset=ml-100k --model=SASRec --method=CL4SRec_XAUG --xai_method=occlusion --gpu_id=2
python run_train.py --dataset=Amazon_Beauty --model=SASRec --method=CL4SRec_XAUG --xai_method=occlusion --gpu_id=12

Command for DuoRec

python run_train.py --dataset=ml-100k --model=SASRec --method=DuoRec --gpu_id=2
python run_train.py --dataset=Amazon_Beauty --model=SASRec --method=DuoRec --gpu_id=12

Command for DuoRec w/ X-Aug (Ours)

python run_train.py --dataset=ml-100k --model=SASRec --method=DuoRec_XAUG --xai_method=occlusion --gpu_id=2
python run_train.py --dataset=Amazon_Beauty --model=SASRec --method=DuoRec_XAUG --xai_method=occlusion --gpu_id=12

Performance

5-core Setting

Experimtents on ML-100K.

| model | Recall@5 | Recall@10 | Recall@20 | NDCG@5 | NDCG@10 | NDCG@20 | |------------------|----------|-----------|-----------|--------|---------|---------| | CL4SRec |0.0753 |0.1273 |0.2216 |0.0452 |0.0617 |0.0856 | |+X-Aug (Occlusion)|0.0806 |0.1389 |0.2418 |0.0454 |0.0642 |0.0899 | | CoSeRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | ContraRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | DuoRec |0.0764 |0.1198 |0.2216 |0.0462 |0.0602 |0.0860 | |+X-Aug (Occlusion)|0.0753 |0.1315 |0.2238 |0.0456 |0.0633 |0.0866 | | ICL | | | | | | | |EC4SRec (Occlusion)| | | | | | |

Experimtents on Amazon_Beauty

| model | Recall@5 | Recall@10 | Recall@20 | NDCG@5 | NDCG@10 | NDCG@20 | |------------------|----------|-----------|-----------|--------|---------|---------| | CL4SRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | CoSeRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | ContraRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | DuoRec | | | | | | | |+X-Aug (Occlusion)| | | | | | | | ICL | | | | | | | |EC4SRec (Occlusion)| | | | | | |

Citations

@article{wang2022explanation,
  title={Explanation Guided Contrastive Learning for Sequential Recommendation},
  author={Wang, Lei and Lim, Ee-Peng and Liu, Zhiwei and Zhao, Tianxiang},
  journal={arXiv preprint arXiv:2209.01347},
  year={2022}
}