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[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

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MCLRec

This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

Environment Requirement

  • Pytorch>=1.7.0
  • Python>=3.7

Model Overview

avator

Usage

Please run the following command to install all the requirements:

pip install -r requirements.txt

Datasets Prepare

Please use the data_process.py under dataset/ to get the input dataset by running the following command :

python data_process.py

Evaluate Model

We provide the trained models on Amazon_Beauty, Amazon_Sports_and_Outdoors, and Yelp datasets in ./log/Checkpoint/<Data_name>folder. You can directly evaluate the trained models on test set by running:

python run_seq.py --dataset=<Data_name> --do_eval

On Amazon_Beauty:

python run_seq.py --dataset=Amazon_Beauty --do_eval
 INFO  test result: {'recall@5': 0.0581, 'recall@10': 0.0871, 'recall@20': 0.1243, 'recall@50': 0.1852, 'mrr@5': 0.0278, 'mrr@10': 0.0316, 'mrr@20': 0.0341, 'mrr@50': 0.036, 'ndcg@5': 0.0352, 'ndcg@10': 0.0446, 'ndcg@20': 0.0539, 'ndcg@50': 0.066, 'precision@5': 0.0116, 'precision@10': 0.0087, 'precision@20': 0.0062, 'precision@50': 0.0037}

On Amazon_Sports_and_Outdoors:

python run_seq.py --dataset=Amazon_Sports_and_Outdoors --do_eval
INFO  test result: {'recall@5': 0.0328, 'recall@10': 0.0501, 'recall@20': 0.0734, 'recall@50': 0.1215, 'mrr@5': 0.0163, 'mrr@10': 0.0186, 'mrr@20': 0.0202, 'mrr@50': 0.0218, 'ndcg@5': 0.0204, 'ndcg@10': 0.026, 'ndcg@20': 0.0319, 'ndcg@50': 0.0414, 'precision@5': 0.0066, 'precision@10': 0.005, 'precision@20': 0.0037, 'precision@50': 0.0024}

On Yelp:

python run_seq.py --dataset=Yelp --do_eval
INFO  test result: {'recall@5': 0.0454, 'recall@10': 0.0647, 'recall@20': 0.0941, 'recall@50': 0.1557, 'mrr@5': 0.0292, 'mrr@10': 0.0317, 'mrr@20': 0.0337, 'mrr@50': 0.0356, 'ndcg@5': 0.0332, 'ndcg@10': 0.0394, 'ndcg@20': 0.0467, 'ndcg@50': 0.0589, 'precision@5': 0.0091, 'precision@10': 0.0065, 'precision@20': 0.0047, 'precision@50': 0.0031}

Train Model

Please train the model using the Python script run_seq.py.

​ You can run the following command to train the model on Yelp datasets:

python run_seq.py --dataset=Yelp --epochs=100 --use_rl=1 --joint=0 train_batch_size=256 --lmd=0.03 --beta=0.1 --sim='dot'
  • or You can cd scripts and run the following command to train the model on different dataset:
bash scrips/train_{dataset name}.sh

bash scripts/train_beauty.sh
bash scripts/train_ml-1m.sh
bash scripts/train_sports.sh
bash scripts/train_toys.sh
bash scripts/train_yelp.sh

Overall Performances

N represents Normalized Discounted Cumulative Gain(NDCG) and H represents Hit Ratio (HR).

Dataset Metrc BPR GRU4Rec Caser SASRec BERT4Rec S3Rec CL4SRec CoSeRec LMA4Rec ICLRec DuoRec SRMA MCLRec Improv.
Sports H@5 0.0123 0.0162 0.0154 0.0214 0.0217 0.0121 0.0231 0.0290 0.0297 0.0290 0.0312 0.0299 0.0328 5.13%
Sports H@20 0.0369 0.0421 0.0399 0.0500 0.0604 0.0344 0.0557 0.0636 0.0634 0.0646 0.0696 0.0649 0.0734 5.46%
Sports N@5 0.0076 0.0103 0.0114 0.0144 0.0143 0.0084 0.0146 0.0196 0.0197 0.0191 0.0192 0.0199 0.0204 2.51%
Sports N@20 0.0144 0.0186 0.0178 0.0224 0.0251 0.0146 0.0238 0.0293 0.0293 0.0291 0.0302 0.0297 0.0319 5.63%
Beauty H@5 0.0178 0.0180 0.0251 0.0377 0.0360 0.0189 0.0401 0.0504 0.0511 0.0500 0.0559 0.0503 0.0581 3.94%
Beauty H@20 0.0474 0.0427 0.0643 0.0894 0.0984 0.0487 0.0974 0.1034 0.1047 0.1058 0.1193 0.1025 0.1243 4.19%
Beauty N@5 0.0109 0.0116 0.0145 0.0241 0.0216 0.0115 0.0268 0.0339 0.0342 0.0326 0.0340 0.0318 0.0352 2.92%
Beauty N@20 0.0192 0.0186 0.0298 0.0386 0.0391 0.0198 0.0428 0.0487 0.0493 0.0483 0.0518 0.0474 0.0539 4.05%
Yelp H@5 0.0127 0.0152 0.0142 0.0160 0.0196 0.0101 0.0227 0.0241 0.0233 0.0239 0.0429 0.0243 0.0454 5.83%
Yelp H@20 0.0346 0.0371 0.0406 0.0443 0.0564 0.0314 0.0623 0.0649 0.0636 0.0659 0.0868 0.0646 0.0941 8.41%
Yelp N@5 0.0082 0.0091 0.0080 0.0101 0.0121 0.0068 0.0143 0.0151 0.0147 0.0152 0.0324 0.0154 0.0332 2.47%
Yelp N@20 0.0143 0.0145 0.0156 0.0179 0.0223 0.0127 0.0254 0.0263 0.0258 0.0270 0.0447 0.0266 0.0467 4.47%

Future Work

  • Add MCL on Graph Recommendation
  • Building Augmenters Using Different Structures

Acknowledgment

  • Transformer and training pipeline are implemented based on Recbole. Thanks them for providing efficient implementation.

Citation

@inproceedings{MCLRec,
  author       = {Xiuyuan Qin and
                  Huanhuan Yuan and
                  Pengpeng Zhao and
                  Junhua Fang and
                  Fuzhen Zhuang and
                  Guanfeng Liu and
                  Yanchi Liu and
                  Victor S. Sheng},
  title        = {Meta-optimized Contrastive Learning for Sequential Recommendation},
  booktitle    = {SIGIR},
  pages        = {89--98},
  year         = {2023},
}
  • Please kindly cite our paper if this paper and the code are helpful.

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[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

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