Interactions can be downloaded from https://drive.google.com/file/d/1kKFfVi_SWPPHb3pVpoDP88QNBZS3tv1R/view?usp=drive_link, containing Pixel200K, Pixel1M, Pixel8M and PixelRec. The item popularity distributions are shown as follows :
For more detailed statistics of interactions, see dataset/statistics.
We currently provide a sampled version including 50,000 users. The complete dataset will be made publicly available later. The sampled dataset can be obtained through:
Note that this is an image recommendation dataset, if you need video information, please go to our MicroLens github （https://github.com/westlake-repl/MicroLens）, a large-scale micro-video recommendation dataset collected from a different platform.
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google drive:
https://drive.google.com/drive/folders/1bQPgM-6yAnzcD0jKBoUUheA9LL5xnCHG?usp=sharing
& https://drive.google.com/file/d/1SZ7hj1HDVCV_WDidSOFGgCVhx1rpWE31/view?usp=sharing
Due to the file size upload limit, we have evenly divided the image files into two folders, i.e. cover1 and cover2 in the link.
See dataset/item_info.csv.
The item_info.csv contains item features in PixelRec, the column names of the file are as follows:
| item_id | view_number | comment_number | thumbup_number | share_number | coin_number | favorite_number | barrage_number | title | tag |
|---|
⚠️ Caution: It's prohibited to privately modify the dataset and offer secondary downloads. If you've made alterations to the dataset in your work, you are encouraged to open-source the data processing code, so others can benefit from your methods.
Pytorch==1.10.2
cudatoolkit==11.2.1
python==3.9.7
See requirements.txt for other packages:
pip install -r requirements.txtTo run the baselines:
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Download the interaction data and images.
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Generate lmdb database from the images:
cd code && python generate_lmdb.py- You can choose different
yamlfiles to run different baselines, theyamlfiles are under foldersIDNet,PixelNet,ViNetandoverall
To run IDNet, for example, run SASRec model on one card:
python main.py --device 0 --config_file IDNet/sasrec.yaml overall/ID.yamlChange the IDNet/sasrec.yaml to run other IDNet baselines.
To run PixelNet, for example, run SASRec model with ViT encoders on four cards:
python main.py --device 0,1,2,3 --config_file PixelNet/sasrec.yaml overall/ViT.yamlChange PixelNet/sasrec.yaml to run other PixelNet baselines with ViT as item encoder, change overall/ViT.yaml to run sasrec model with other image encoders.
To run ViNet, e.g. run VBPR model on one card:
python main.py --device 0 --config_file ViNet/vbpr.yamlChange ViNet/vbpr.yaml to run other ViNet
Note: you may need to modify some path in files under folders ViNet and overall and file generate_lmdb.py , depending on where you put the downloaded data.
Hyper parameter range :
embedding size [128, 512, 1024, 2048, 4096, 8192]
learning rate [0.000001, 0.00005, ... , 0.001]
weight decay [0, 0.01, 0.1]
batch size [64, 128, 256, 512, 1024]
Hyper-parameter details of IDNet.
| Method (IDNet) | Model Parameters | Training Parameters |
|---|---|---|
| MF | dropout prob [0] embedding size [4096] | γ [0.0001] B [512] β [0] |
| FM | embedding size [4096] | γ [0.00005] B [64] β [0] |
| DSSM | dnn layer number [0] embedding size [4096] | γ [0.0001] B [64] β [0] |
| LightGCN | step [2] embedding size [256] | γ [0.0005] B [1024] β [0.01] |
| SASRec | trm layer number [2] inner size [2] embedding size [512] | γ [0.00005] B [64] β [0.1] |
| BERT4Rec | mask ratio [0.6] trm layer number [2] inner size [1] embedding size [512] | γ [0.00005] B [64] β [0.1] |
| LightSANs | k [3] trm layer number [1] embedding size [512] | γ [0.00005] B [512] β [0.1] |
| GRU4Rec | dropout prob [0] gru layer number [1] inner size [2] embedding size [2048] | γ [0.0001] B [64] β [0.01] |
| NextItNet | block number [3] embedding size [1024] | γ [0.0005] B [64] β [0.01] |
| SRGNN | step [2] embedding size [512] | γ [0.00005] B [64] β [0.01] |
| VisRank | visual feature [RN_2048] method [maximum] | |
| VBPR | id γ [0.001] id β [0] visual γ [0.0001] visual β [0.1] | |
| ACF | embedding size [128] | γ [0.0001] B [64] β [0.1] |
For the most architectures, PixelNet uses the same hyperparameters as its IDNet, with a few exceptions here. The embedding size refers to the hidden dimension of the user encoder.
| Method (PixelNet) | Model Parameters | Training Parameters |
|---|---|---|
| SASRec | trm layer number [2] inner size [2] embedding size [512] | γ [0.0001] B [64] β [0.1] |
| BERT4Rec | mask ratio [0.6] trm layer number [2] inner size [1] embedding size [512] | γ [0.0001] B [64] β [0.1] |
| LightSANs | k [3] trm layer number [1] embedding size [512] | γ [0.0001] B [512] β [0.1] |
| NextItNet | block number [3] embedding size [1024] | γ [0.0001] B [64] β [0.01] |
| SRGNN | step [2] embedding size [512] | γ [0.0001] B [512] β [0.01] |
In PixelNet, we adopt different learning rate and weight decay between the image encoder and the rest of the model structures. Here are the hyper-parameter for tuning the image encoders.
| Image Encoder | Hyper Parameter |
|---|---|
| RN50, RN50x4, RN50x16, RN50x64, ResNet50 | γ [0.0001] β [0.01] |
| ViT, Swin-T, Swin-B, BEiT | γ [0.0001] β [0] |
If our work has been of assistance to your work, please cite our paper as :
@article{cheng2023image,
title={An Image Dataset for Benchmarking Recommender Systems with Raw Pixels},
author={Cheng, Yu and Pan, Yunzhu and Zhang, Jiaqi and Ni, Yongxin and Sun, Aixin and Yuan, Fajie},
journal={arXiv preprint arXiv:2309.06789},
year={2023}
}
|MicroLens (A short video recommendation dataset) | https://github.com/westlake-repl/MicroLens |
|Tenrec (A dataset covering 10 different recommendation tasks) | https://github.com/yuangh-x/2022-NIPS-Tenrec |