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.

翻译：跨领域推荐近年来引起了工业界和学术界的广泛关注。然而，现有大多数方法并未利用领域间的兴趣不变性，这会导致次优解的产生。本文提出一种跨领域推荐方法：自监督兴趣迁移网络（SITN），该方法通过原型对比学习有效迁移领域间的不变知识。具体而言，我们执行了两个层次的跨领域对比学习：1）实例间对比学习；2）实例到簇对比学习。不仅如此，我们还考虑了用户的多粒度与多视角兴趣。通过这一范式，SITN能够显式学习领域间兴趣簇的不变知识，并准确捕捉用户的意图与偏好。我们在公共数据集及从世界领先电商企业收集的大规模工业数据集上进行了广泛实验。实验结果表明，SITN在推荐性能上显著优于现有最优方法。此外，SITN已部署于微视频推荐平台，线上A/B测试结果进一步验证了其实用价值。补充材料见：https://github.com/fanqieCoffee/SITN-Supplement。