We have released all 9 downstream datasets, and we will provide access to the source dataset once the paper is accepted. To acquire the complete NineRec dataset, kindly reach out to the corresponding author via email. If you have an innovative idea for building a foundational recommendation model but require a large dataset and computational resources, consider joining our lab as an intern. We can provide access to 100 NVIDIA 80G A100 GPUs and a billion-level dataset of user-video/image/text interactions.

Download link:

• Google Drive: https://drive.google.com/file/d/1C58i4o4S-Bxwu3MgqQn7S0lHGzg5BgV6/view?usp=sharing

If you are interested in conducting pre-training, you can find a relatively large image dataset available at https://github.com/westlake-repl/IDvs.MoRec. Please follow the provided instructions to utilize the dataset properly, as it is not fully published yet. If you want to pre-train on a very large-scale image/video/text dataset for a foundation Recsys model, contact our leading authors by email.

If you use our dataset, code or find NineRec useful in your work, please cite our paper as:

@article{zhang2023ninerec,
      title={NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation}, 
      author={Jiaqi Zhang and Yu Cheng and Yongxin Ni and Yunzhu Pan and Zheng Yuan and Junchen Fu and Youhua Li and Jie Wang and Fajie Yuan},
      journal={arXiv preprint arXiv:2309.07705},
      year={2023}
}

⚠️ 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==1.12.1
cudatoolkit==11.2.1
sklearn==1.2.0
python==3.9.12

Run get_lmdb.py to get lmdb database for image loading. Run get_behaviour.py to convert the user-item pairs into item sequences format.

Run train.py for pre-training and transferring. Run test.py for testing.

coming soon.

Tenrec (https://github.com/yuangh-x/2022-NIPS-Tenrec) is the sibling dataset of NineRec, which includes multiple user feedback and platforms. It is suitable for studying ID-based transfer and lifelong learning.

实验室招聘科研助理、实习生、博士生和博后，请联系通讯作者。