Disentangling Long and Short-Term Interests for Recommendation

@article{Zheng2022DisentanglingLA,
  title={Disentangling Long and Short-Term Interests for Recommendation},
  author={Y. Zheng and Chen Gao and Jianxin Chang and Yanan Niu and Yang Song and Depeng Jin and Yong Li},
  journal={Proceedings of the ACM Web Conference 2022},
  year={2022}
}
  • Y. Zheng, Chen Gao, Yong Li
  • Published 26 February 2022
  • Computer Science
  • Proceedings of the ACM Web Conference 2022
Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may lead to inferior recommendation accuracy and interpretability. In this paper, to address it, we propose a Contrastive learning framework to disentangle Long and Short-term interests for Recommendation (CLSR) with self-supervision. Specifically… 

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