Tenrec is a large-scale multipurpose benchmark dataset for recommender systems where data was collected from two feeds (articles and videos) recommendation platforms.
QK-video.csv: User video action in QK.
QB-video.csv: User video action in QB.
QK-article.csv: User article action in QK.
QB-artilce.csv: User article action in QB.
Download the dataset:
Dataset link: https://static.qblv.qq.com/qblv/h5/algo-frontend/tenrec_dataset.html
Leaderboard link: https://tenrec0.github.io/
Please check your web setting if you cannot access the official download link. (It should be fine since we have tested many VPN settings.) You should download the dataset from our official website and accept the licence agreement, wherever you get the dataset and use it for your publication.
We apply Tenrec on 10 recommendation tasks. There are more tasks (e.g., Top-N recommendation), settings and results (including original large datasets) present in our paper appendix (see openreview). Please run the commands as below to test the performance of each task.
If you use Tenrec (with our training, validation and testing set) and have new SOTA results, we are happy to update them on the leaderboard. Make sure your models are evaluated with a fair comparison or common practice. E.g., if you compare the network architecture, you should ensure that you loss functions and sampling are the same with the baseline. We are also happy to create new leaderboard if you use Tenrec to perform new tasks, just email us.
AFM
python main.py --task_name=ctr --seed=100 --model_name=afm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
DeepFM
python main.py --task_name=ctr --seed=100 --model_name=deepfm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
xDeepFM
python main.py --task_name=ctr --seed=100 --model_name=xdeepfm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
NFM
python main.py --task_name=ctr --seed=100 --model_name=nfm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
Wide & Deep
python main.py --task_name=ctr --seed=100 --model_name=wdl --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
DCN
python main.py --task_name=ctr --seed=100 --model_name=dcn --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
DCNv2
python main.py --task_name=ctr --seed=100 --model_name=dcnmix --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
DIN
python main.py --task_name=ctr --seed=100 --model_name=din --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
DIEN
python main.py --task_name=ctr --seed=100 --model_name=dien --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.00005
NextItNet
python main.py --task_name=sequence --seed=100 --model_name=nextitnet --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=1
BERT4Rec
python main.py --task_name=sequence --seed=100 --model_name=bert4rec --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --bert_mask_prob=0.3 --is_pretrain=1
SASRec
python main.py --task_name=sequence --seed=100 --model_name=sasrec --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=8 --embedding_size=64 --num_heads=4 --is_pretrain=1
GRU4Rec
python main.py --task_name=sequence --seed=100 --model_name=gru4rec --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0005 --hidden_size=64 --block_num=8 --embedding_size=64 --is_pretrain=1
Only click
python main.py --task_name=mtl --seed=100 --model_name=mmoe --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --val_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.0001 --embedding_size=32 ----mtl_task_num=1
Only like
python main.py --task_name=mtl --seed=100 --model_name=mmoe --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --val_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.0001 --embedding_size=32 ----mtl_task_num=0
ESMM
python main.py --task_name=mtl --seed=100 --model_name=esmm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --val_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.0001 --embedding_size=32 ----mtl_task_num=2
MMOE
python main.py --task_name=mtl --seed=100 --model_name=esmm --dataset_path='data/ctr_data_1M.csv' --train_batch_size=4096 --val_batch_size=4096 --test_batch_size=4096 --epochs=20 --lr=0.0001 --embedding_size=32 ----mtl_task_num=2
Plese run the command of Session-based Recommendation Task firstly.
NextItNet with Pretrain
python main.py --task_name=transfer_learning --seed=100 --model_name=peterrec --dataset_path='data/QB-video.csv' --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0005 --hidden_size=128 --block_num=16 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0
SASRec with Pretrain
python main.py --task_name=transfer_learning --seed=100 --model_name=sas4transfer --dataset_path='data/QB-video.csv' --pretrain_path='checkpoint/sequence_sasrec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd64_emb64.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=8 --embedding_size=64 --num_heads=4 --is_pretrain=0
Plese run the command of Session-based Recommendation Task firstly.
DNN
python main.py --task_name=user_profile_represent --seed=100 --model_name=dnn4profile --dataset_path=data/sbr_data_1M.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=16 --embedding_size=128 --is_pretrain=2
BERT4Rec without Pretrain
python main.py --task_name=user_profile_represent --seed=100 --model_name=bert4profile --dataset_path=data/sbr_data_1M.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=2
Peterrec without Pretrain
python main.py --task_name=user_profile_represent --seed=100 --model_name=peter4profile --dataset_path=data/sbr_data_1M.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.00005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=2
BERT4Rec with Pretrain
python main.py --task_name=user_profile_represent --seed=100 --model_name=bert4profile --dataset_path=data/sbr_data_1M.csv --pretrain_path='checkpoint/sequence_bert4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block16_hd128_emb128.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=0
Peterrec with Pretrain
python main.py --task_name=user_profile_represent --model_name=peter4profile --dataset_path=data/sbr_data_1M.csv --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.00005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0
##cold_data.csv
BERT4Rec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data.csv --model_name=bert4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=2 --ch=False
Peterrec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data.csv --model_name=peter4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=2 --ch=False
Please run the command of Session-based Recommendation Task firstly.
BERT4Rec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=bert4coldstart --pretrain_path='checkpoint/sequence_bert4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block16_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=0 --ch=False
Peterrec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=peter4coldstart --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0 --ch=False
##cold_data_1.csv
BERT4Rec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_1.csv --model_name=bert4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=2 --ch=False
Peterrec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_1.csv --model_name=peter4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=2 --ch=False
Please run the command of Session-based Recommendation Task firstly.
BERT4Rec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=bert4coldstart --pretrain_path='checkpoint/sequence_bert4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block16_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_1.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.001 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=0 --ch=False
Peterrec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=peter4coldstart --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_1.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0 --ch=False
##cold_data_0.7.csv
BERT4Rec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.7.csv --model_name=bert4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=2 --ch=True
Peterrec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.7.csv --model_name=peter4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=2 --ch=True
Please run the command of Session-based Recommendation Task firstly.
BERT4Rec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=bert4coldstart --pretrain_path='checkpoint/sequence_bert4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block16_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.7.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=0 --ch=True
Peterrec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=peter4coldstart --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.7.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0 --ch=True
##cold_data_0.3.csv
BERT4Rec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.3.csv --model_name=bert4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=2 --ch=True
Peterrec without Pretrain
python main.py --task_name=cold_start --seed=10 --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.3.csv --model_name=peter4coldstart --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=2 --ch=True
Please run the command of Session-based Recommendation Task firstly.
BERT4Rec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=bert4coldstart --pretrain_path='checkpoint/sequence_bert4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block16_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.3.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=16 --embedding_size=128 --num_heads=4 --is_pretrain=0 --ch=True
Peterrec with Pretrain
python main.py --task_name=cold_start --seed=10 --model_name=peter4coldstart --pretrain_path='checkpoint/sequence_nextitnet_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block8_hd128_emb128.pth' --source_path=data/sbr_data_1M.csv --target_path=data/cold_data_0.3.csv --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=5e-5 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0 --ch=True
SAS4Rec
python main.py --task_name=life_long --seed=100 --task_num=4 --model_name=sas4life --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --re_epochs=20 --lr=0.0001 --hidden_size=64 --block_num=8 --embedding_size=64 --num_heads=4
Conure
python main.py --task_name=life_long --seed=100 --task_num=4 --model_name=conure --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --re_epochs=20 --lr=0.0001 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3
SASRec
python main.py --task_name=model_compr --seed=100 --model_name=sas4cp --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=8 --embedding_size=64 --num_heads=4 --is_pretrain=1
Cprec
python main.py --task_name=model_compr --seed=100 --model_name=cprec --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=1
SASRec-shallow train
python main.py --task_name=model_acc --seed=100 --model_name=sas4acc --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=4 --embedding_size=64 --num_heads=4 --is_pretrain=1
SASRec-deep train
python main.py --task_name=model_acc --seed=100 --model_name=sas4acc --dataset_path='data/sbr_data_1M.csv' --pretrain_path='checkpoint/model_acc_sas4rec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block4_hd64_emb64.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=4 --embedding_size=64 --num_heads=4 --is_pretrain=0 --add_num_times=2
Stackrec-shallow train
python main.py --task_name=model_acc --seed=100 --model_name=stackrec --dataset_path='data/sbr_data_1M.csv' --pretrain_path='checkpoint/model_acc_stackrec_seed100_is_pretrain_1_best_model_lr0.0001_wd0.0_block4_hd128_emb128.pth' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=4 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=1
Stackrec-deep train
python main.py --task_name=model_acc --seed=100 --model_name=stackrec --dataset_path='data/sbr_data_1M.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=4 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=0 --add_num_times=2
SASRec
python main.py --task_name=inference_acc --seed=5 --model_name=sas4infacc --dataset_path='data/QB-video.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=1 --epochs=20 --lr=0.0001 --hidden_size=64 --block_num=8 --embedding_size=64 --num_heads=4 --is_pretrain=1
Skiprec
python main.py --task_name=inference_acc --seed=5 --model_name=cprec --dataset_path='data/QB-video.csv' --train_batch_size=32 --val_batch_size=32 --test_batch_size=1 --epochs=20 --lr=0.0001 --hidden_size=128 --block_num=8 --embedding_size=128 --dilation=[1, 4] --kernel_size=3 --is_pretrain=1
MF-random_sampler
python main.py --task_name=cf --seed=0 --model_name=mf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='uniform' --num_ng=4 --loss_type='BPR'
MF-popularity_sampler
python main.py --task_name=cf --seed=0 --model_name=mf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='high-pop' --sample_ratio=0.3 --num_ng=4 --loss_type='BPR'
NCF-random_sampler
python main.py --task_name=cf --seed=0 --model_name=ncf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.000001 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='uniform' --num_ng=4 --loss_type='BPR'
NCF-popularity_sampler
python main.py --task_name=cf --seed=0 --model_name=ncf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.000001 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='high-pop' --sample_ratio=0.3 --num_ng=4 --loss_type='BPR'
NGCF-random_sampler
python main.py --task_name=cf --seed=0 --model_name=ngcf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='uniform' --num_ng=4 --loss_type='BPR' --node_dropout=0.1 --mess_dropout=0.1 --hidden_size_list=[128, 128]
NGCF-popularity_sampler
python main.py --task_name=cf --seed=0 --model_name=ngcf --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.0005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='high-pop' --sample_ratio=0.3 --num_ng=4 --loss_type='BPR' --node_dropout=0.1 --mess_dropout=0.1 --hidden_size_list=[128, 128]
LightGCN-random_sampler
python main.py --task_name=cf --seed=0 --model_name=lightgcn --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='uniform' --num_ng=4 --loss_type='BPR'
LightGCN-popularity_sampler
python main.py --task_name=cf --seed=0 --model_name=lightgcn --dataset_path='data/QB-video.csv' --train_batch_size=4096 --val_batch_size=32 --test_batch_size=32 --epochs=20 --lr=0.005 --factor=128 --block_num=2 --test_method='ufo' --val_method='ufo' --test_size=0.1 --val_size=0.1111 --sample_method='high-pop' --sample_ratio=0.3 --num_ng=4 --loss_type='BPR'
Pytorch 1.7.0
Tensorflow 2.3.0
sklearn 0.24.2
python 3.6.8
We refer to deppCTR, Recbole and DaisyRec for some model implementation in the CTR, SBR and Top-N tasks.
Recbole: https://recbole.io, DeepCTR: https://github.com/shenweichen/DeepCTR, DaisyRec: https://github.com/recsys-benchmark/DaisyRec-v2.0.
License:
This dataset is licensed under a CC BY-NC 4.0 International License(https://creativecommons.org/licenses/by-nc/4.0/).