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Multi-Modal Foundation-Model Video-Understanding Video-Generation Video-Recommendation

Quick Links: 🗃️Dataset | 📭Citation | 🛠️Code | 🚀Baseline Evaluation | 🤗Video Understanding Meets Recommender Systems | 💡News

Talks & Slides: Invited Talk by Google DeepMind & YouTube & Alipay (Slides)

Dataset

Download links: https://recsys.westlake.edu.cn/MicroLens-50k-Dataset/ and https://recsys.westlake.edu.cn/MicroLens-100k-Dataset/

Email us if you find the link is not available.

News

Citation

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

@article{ni2023content,
  title={A Content-Driven Micro-Video Recommendation Dataset at Scale},
  author={Ni, Yongxin and Cheng, Yu and Liu, Xiangyan and Fu, Junchen and Li, Youhua and He, Xiangnan and Zhang, Yongfeng and Yuan, Fajie},
  journal={arXiv preprint arXiv:2309.15379},
  year={2023}
}

⚠️ Caution: It's prohibited to privately modify the dataset and then 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. Or notify us of your new dataset so we can put it on this Github with your paper.

Code

We have released the codes for all algorithms, including VideoRec (which implements all 15 video models in this project), IDRec, and VIDRec. For more details, please refer to the following paths: "Code/VideoRec", "Code/IDRec", and "Code/VIDRec". Each folder contains multiple subfolders, with each subfolder representing the code for a baseline.

Special instructions on VideoRec

In VideoRec, if you wish to switch to a different training mode, please execute the following Python scripts: 'run_id.py', 'run_text.py', 'run_image.py', and 'run_video.py'. For testing, you can use 'run_id_test.py', 'run_text_test.py', 'run_image_test.py', and 'run_video_test.py', respectively. Please see the path "Code/VideoRec/SASRec" for more details.

Before running the training script, please make sure to modify the dataset path, item encoder, pretrained model path, GPU devices, GPU numbers, and hyperparameters. Additionally, remember to specify the best validation checkpoint (e.g., 'epoch-30.pt') before running the test script.

Note that you will need to prepare an LMDB file and specify it in the scripts before running image-based or video-based VideoRec. To assist with this, we have provided a Python script for LMDB generation. Please refer to 'Data Generation/generate_cover_frames_lmdb.py' for more details.

Special instructions on IDRec and VIDRec

In IDRec, see IDRec\process_data.ipynb to process the interaction data. Execute the following Python scripts: 'main.py' under each folder to run the corresponding baselines. The data path, model parameters can be modified by changing the yaml file under each folder.

Environments

python==3.8.12
Pytorch==1.8.0
cudatoolkit==11.1
torchvision==0.9.0
transformers==4.23.1

Baseline_Evaluation

Video_Understanding_Meets_Recommender_Systems

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The laboratory is hiring research assistants, interns, doctoral students, and postdoctoral researchers. Please contact the corresponding author for details.

实验室招聘科研助理,实习生,博士生和博士后,请联系通讯作者。