Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation

TL;DR

This paper introduces a novel framework for short-video recommendation that models heterogeneous multi-treatment effects to optimize trade-offs between conflicting user engagement metrics, enabling personalized and effective decision-making.

Contribution

The paper proposes a new Heterogeneous Multi-treatment Uplift Modeling framework combining offline and online modules for better trade-off optimization in short-video recommendations.

Findings

Superior offline performance on multiple datasets

Significant improvements in key engagement metrics

Full deployment on Kuaishou platform benefiting millions

Abstract

The rapid proliferation of short videos on social media platforms presents unique challenges and opportunities for recommendation systems. Users exhibit diverse preferences, and the responses resulting from different strategies often conflict with one another, potentially exhibiting inverse correlations between metrics such as watch time and video view counts. Existing uplift models face limitations in handling the heterogeneous multi-treatment scenarios of short-video recommendations, often failing to effectively capture both the synergistic and individual causal effects of different strategies. Furthermore, traditional fixed-weight approaches for balancing these responses lack personalization and can result in biased decision-making. To address these issues, we propose a novel Heterogeneous Multi-treatment Uplift Modeling (HMUM) framework for trade-off optimization in short-video recommendations. HMUM comprises an Offline Hybrid Uplift Modeling (HUM) module, which captures the synergistic and individual effects of multiple strategies, and an Online Dynamic Decision-Making (DDM) module, which estimates the value weights of different user responses in real-time for personalized decision-making. Evaluated on two public datasets, an industrial dataset, and through online A/B experiments on the Kuaishou platform, our model demonstrated superior offline performance and significant improvements in key metrics. It is now fully deployed on the platform, benefiting hundreds of millions of users.