KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
<p>The details can be referred to: <a href="https://kuairand.com/" target="_blank" rel="noopener"><strong>https://kuairand.com/</strong></a></p> <p>If it helps you, please kindly cite:</p> <blockquote> <pre><code>@inproceedings{gao2022kuairand, title = {KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos}, author = {Gao, Chongming and Li, Shijun and Zhang, Yuan and Chen, Jiawei and Li, Biao and Lei, Wenqiang and Jiang, Peng and He, Xiangnan}, url = {https://doi.org/10.1145/3511808.3557624}, doi = {10.1145/3511808.3557624}, booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management}, series = {CIKM '22}, location = {Atlanta, GA, USA}, numpages = {5}, year = {2022}, pages = {3953–3957} }</code></pre> </blockquote>
ShareScore
24/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 0