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Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Tenrec is a large-scale multipurpose benchmark dataset for recommender systems where data was collected from two feeds (articles and videos) recommendation platforms of Tencent.

Dataset in Tenrec:

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

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.

Benchmark

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. In this case, you should provide (1) your algorithm code; (2) all your hyper-parameters; (3) a readme file tells other researchers how to use your code. We will append them on the leaderboard website, and 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.

CTR (including shared embedding and separate embedding, see paper appendix):

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

Session-based Recommendation

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

Multi-Task Learing

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

Transfer Learning

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

User Profile Prediction

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-start Recommendation

##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

Lifelong User Representation Learning

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

Model Compression

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

Model Training Speedup

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 

Model Inference Speedup

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 

Top-N

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' 

Environments

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/).

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