Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei
Abstract
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: https://github.com/fanqieCoffee/SITN-Supplement.
BibTeX
@article{Sun_Shen_Zhou_Chen_Liu_Wu_Lei_Wei_Fang_2023, title={Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25584}, DOI={10.1609/aaai.v37i4.25584}, abstractNote={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: https://github.com/fanqieCoffee/SITN-Supplement.}, number={4}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sun, Guoqiang and Shen, Yibin and Zhou, Sijin and Chen, Xiang and Liu, Hongyan and Wu, Chunming and Lei, Chenyi and Wei, Xianhui and Fang, Fei}, year={2023}, month={Jun.}, pages={4614-4622} }