Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation


  • Guoqiang Sun Zhejiang University Alibaba Group
  • Yibin Shen Alibaba Group
  • Sijin Zhou Alibaba Group
  • Xiang Chen Zhejiang University
  • Hongyan Liu Zhejiang University
  • Chunming Wu Zhejiang University
  • Chenyi Lei Alibaba Group
  • Xianhui Wei Alibaba Group
  • Fei Fang Alibaba Group



DMKM: Recommender Systems, ML: Unsupervised & Self-Supervised Learning


Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between domains, which would yield sub-optimal solutions. In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning. Specifically, we perform two levels of cross-domain contrastive learning: 1) instance-to-instance contrastive learning, 2) instance-to-cluster contrastive learning. Not only that, we also take into account users' multi-granularity and multi-view interests. With this paradigm, SITN can explicitly learn the invariant knowledge of interest clusters between domains and accurately capture users' intents and preferences. We conducted extensive experiments on a public dataset and a large-scale industrial dataset collected from one of the world's leading e-commerce corporations. The experimental results indicate that SITN achieves significant improvements over state-of-the-art recommendation methods. Additionally, SITN has been deployed on a micro-video recommendation platform, and the online A/B testing results further demonstrate its practical value. Supplement is available at:




How to Cite

Sun, G., Shen, Y., Zhou, S., Chen, X., Liu, H., Wu, C., Lei, C., Wei, X., & Fang, F. (2023). Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4614-4622.



AAAI Technical Track on Data Mining and Knowledge Management