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We release the pre-trained models for reproducibility.

{data-name}-epochs-{pretrain_epochs_num}.pt

The log files are also released and the '-0' means without pre-training.

Finetune_sample-{data-name}-epochs-0.pt
Finetune_sample-{data-name}-epochs-{pretrain_epochs_num}.pt

Note

There is a minor bug in the old version codes, which is that we did not set the random seed for all random methods. We are very sorry for this error. And we deleted pre-trained models considering the disk space. So we re-run the code and get new results, which could be considered as reproduced the results in the paper.

When you fine-tune the model, please check the log information. If it is

ckp_file Not Found! The Model is the same as SASRec.

then you actually run the SASRec and the model's parameters are initialized randomly. Otherwise, you would see

Load Checkpoint From ckp_path!

which means you successfully initialize the model with pre-trained parameters.

Meituan

Considering the data security issues, this dataset will not release.

Beauty

Model HR@1 HR@5 NDCG@5 HR@10 NDCG@10 MRR
SASRec in paper 0.1870 0.3741 0.2848 0.4696 0.3156 0.2852
SASRec in repo 0.1867 0.3721 0.2843 0.4651 0.3142 0.2850
S3-Rec in paper 0.2192 0.4502 0.3407 0.5506 0.3732 0.3340
S3-Rec in repo 0.2197 0.4626 0.3473 0.5687 0.3816 0.3390
  • pretrain (just use the default hyper-parameters)
python run_pretrain.py \
--data_name Beauty
  • finetune (just use the default hyper-parameters)
python run_finetune_sample.py \
--data_name Beauty \
--ckp 150

Sports_and_Outdoors

Model HR@1 HR@5 NDCG@5 HR@10 NDCG@10 MRR
SASRec in paper 0.1455 0.3466 0.2497 0.4622 0.2869 0.2520
SASRec in repo 0.1472 0.3441 0.2487 0.4645 0.3875 0.2524
S3-Rec in paper 0.1841 0.4267 0.3104 0.5614 0.3538 0.3071
S3-Rec in repo 0.1840 0.4319 0.3125 0.5664 0.3559 0.3084
  • pretrain (just use the default hyper-parameters)
python run_pretrain.py \
--data_name Sports_and_Outdoors
  • finetune (just use the default hyper-parameters)
python run_finetune_sample.py \
--data_name Sports_and_Outdoors \
--ckp 100

Toys_and_Games

Model HR@1 HR@5 NDCG@5 HR@10 NDCG@10 MRR
SASRec in paper 0.1878 0.3682 0.2820 0.4663 0.3136 0.2842
SASRec in repo 0.1775 0.3683 0.2766 0.4659 0.3081 0.2770
S3-Rec in paper 0.2003 0.4420 0.3270 0.5530 0.3629 0.3202
S3-Rec in repo 0.2070 0.4481 0.3335 0.5593 0.3695 0.3268
  • pretrain (just use the default hyper-parameters)
python run_pretrain.py \
--data_name Toys_and_Games
  • finetune (just use the default hyper-parameters)
python run_finetune_sample.py \
--data_name Toys_and_Games \
--ckp 150

Yelp

Model HR@1 HR@5 NDCG@5 HR@10 NDCG@10 MRR
SASRec in paper 0.2375 0.5745 0.4113 0.7373 0.4642 0.3927
SASRec in repo 0.2310 0.5638 0.4017 0.7384 0.4582 0.3856
S3-Rec in paper 0.2591 0.6085 0.4401 0.7725 0.4934 0.4190
S3-Rec in repo 0.2665 0.6195 0.4492 0.7818 0.5019 0.4270
  • pretrain (just use the default hyper-parameters)
python run_pretrain.py \
--data_name Yelp
  • finetune (just use the default hyper-parameters)
python run_finetune_sample.py \
--data_name Yelp \
--ckp 100

LastFM

Model HR@1 HR@5 NDCG@5 HR@10 NDCG@10 MRR
SASRec in paper 0.1211 0.3385 0.2330 0.4706 0.2755 0.2364
SASRec in repo 0.1156 0.3092 0.2126 0.4587 0.2605 0.2209
S3-Rec in paper 0.1743 0.4523 0.3156 0.5835 0.3583 0.3072
S3-Rec in repo 0.1569 0.4477 0.3056 0.6083 0.3577 0.2981
  • pretrain (just use the default hyper-parameters)
python run_pretrain.py \
--data_name LastFM
  • finetune (just use the default hyper-parameters)
python run_finetune_sample.py \
--data_name LastFM \
--ckp 150