MCLRec

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

• paper
• video
• note

Environment Requirement

• Pytorch>=1.7.0
• Python>=3.7

Model Overview

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

• [x] Add MCL on Graph Recommendation
• [x] 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.