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.
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本文提出了一种跨域推荐方法：自我监督的兴趣转移网络（SITN），可以通过原型对比学习有效地在域之间传输不变知识，同时考虑到用户的多粒度和多视角兴趣，明确了解用户的意图和偏好，实验证明 SITN 可以显著提高推荐方法的性能，进一步证明其实用价值