A large-scale datasets for session-based recommendation and sequential recommendation
Video-6M: https://drive.google.com/file/d/1wd3xzF9VnZ6r35nMb3-H4E31vWK87vjW/view?usp=sharing
We construct a large-scale session-based (sequential) recommendation dataset (denoted as Video-6M) by collecting the interactiton behaviors of nearly 6 million users in a week from a commercial recommender system. The dataset can be used to evaluate ulta deep recommendation models (up to 100 layers), such as NextItNet (as shown in our paper StackRec（SIGIR2021)). If you use this dataset in your paper, you should cite our NextItNet and StackRec for publish permission.
@article{wang2020stackrec,
title={StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking},
author={Wang, Jiachun and Yuan, Fajie and Chen, Jian and Wu, Qingyao and Li, Chengmin and Yang, Min and Sun, Yang and Zhang, Guoxiao},
journal={Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval},
year={2021}
}
@article{yuan2019simple,
title={A simple convolutional generative network for next item recommendation},
author={Yuan, Fajie and Karatzoglou, Alexandros and Arapakis, Ioannis and Jose, Joemon M and He, Xiangnan},
journal={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
year={2019}
}
@inproceedings{chen2021user,
title={A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models},
author={Chen, Lei and Yuan, Fajie and Yang, Jiaxi and Ao, Xiang and Li, Chengming and Yang, Min},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={5},
pages={3984--3991},
year={2021}
}