Title: Prototypical Knowledge Transfer for Multi-Scenario Recommendation with Optimal Transport
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Multi-Scenario Recommendation, Optimal Transport

Abstract: Modern APPs often need to provide personalized recommendations across various scenarios, such as the homepage, local page, and live stream on TikTok. User behaviors in these scenarios differ, resulting in diverse data distributions. To effectively handle varying distributions and enhance performance, current Multi-Scenario Recommendation (MSR) methods usually employ parameter sharing for knowledge transfer across scenarios. However, when there are significant differences in scenario distributions, direct parameter sharing fails to achieve effective knowledge transfer and feature alignment. In this paper, we propose a Prototypical Knowledge Transfer (PKT) method with Optimal Transport for Multi-Scenario Recommendation. Specifically, PKT first employs the Multi-gate Mixture of Experts (MMoE) as a shared feature extractor across all scenarios. We then introduce a prototype layer as an intermediary distribution to serve as a bridge for knowledge transfer between different scenario distributions. Furthermore, we leverage Optimal Transport to facilitate efficient knowledge transfer from scenario distributions to prototypes. To extract scenario-specific features, each scenario is equipped with its own expert network and utilizes the LHUC architecture to enhance scenario-specific features. We conducted offline experiments on two datasets and deployed the method on a video platform, followed by online A/B testing.