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

## TL;DR

This paper introduces SITN, a self-supervised cross-domain recommendation model that leverages prototypical contrastive learning to transfer invariant user interest knowledge, improving recommendation accuracy and practical deployment performance.

## Contribution

The paper proposes a novel self-supervised interest transfer network utilizing dual-level contrastive learning to explicitly model interest invariance across domains.

## Key findings

- SITN outperforms state-of-the-art recommendation methods on public and industrial datasets.
- SITN achieves significant online performance improvements in a micro-video platform.
- The method effectively captures multi-granularity and multi-view user interests.

## 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.