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

• URL: http://arxiv.org/abs/2511.18997v2
• Date: Sat, 29 Nov 2025 10:37:30 GMT
• Title: Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation
• Authors: Chenhao Zhai, Chang Meng, Xueliang Wang, Shuchang Liu, Xiaolong Hu, Shisong Tang, Xiaoqiang Feng, Xiu Li,
• Abstract summary: Short videos on social media platforms present unique challenges and opportunities for recommendation systems.<n>Existing uplift models face limitations in handling the heterogeneous multi-treatment scenarios of short-video recommendations.<n>We propose a novel Heterogeneous Multi-treatment Uplift Modeling framework for trade-off optimization in short-video recommendations.
• Score: 12.779660880397676
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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