Paper
SaFRO: Satisfaction-Aware Fusion via Dual-Relative Policy Optimization for Short-Video Search
Authors
Renzhe Zhou, Songyang Li, Feiran Zhu, Chenglei Dai, Yi Zhang, Yi Wang, Jingwei Zhuo
Abstract
Multi-Task Fusion plays a pivotal role in industrial short-video search systems by aggregating heterogeneous prediction signals into a unified ranking score. However, existing approaches predominantly optimize for immediate engagement metrics, which often fail to align with long-term user satisfaction. While Reinforcement Learning (RL) offers a promising avenue for user satisfaction optimization, its direct application to search scenarios is non-trivial due to the inherent data sparsity and intent constraints compared to recommendation feeds. To this end, we propose SaFRO, a novel framework designed to optimize user satisfaction in short-video search. We first construct a satisfaction-aware reward model that utilizes query-level behavioral proxies to capture holistic user satisfaction beyond item-level interactions. Then we introduce Dual-Relative Policy Optimization (DRPO), an efficient policy learning method that updates the fusion policy through relative preference comparisons within groups and across batches. Furthermore, we design a Task-Relation-Aware Fusion module to explicitly model the interdependencies among different objectives, enabling context-sensitive weight adaptation. Extensive offline evaluations and large-scale online A/B tests on Kuaishou short-video search platform demonstrate that SaFRO significantly outperforms state-of-the-art baselines, delivering substantial gains in both short-term ranking quality and long-term user retention.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19585v1</id>\n <title>SaFRO: Satisfaction-Aware Fusion via Dual-Relative Policy Optimization for Short-Video Search</title>\n <updated>2026-03-20T02:57:50Z</updated>\n <link href='https://arxiv.org/abs/2603.19585v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19585v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Multi-Task Fusion plays a pivotal role in industrial short-video search systems by aggregating heterogeneous prediction signals into a unified ranking score. However, existing approaches predominantly optimize for immediate engagement metrics, which often fail to align with long-term user satisfaction. While Reinforcement Learning (RL) offers a promising avenue for user satisfaction optimization, its direct application to search scenarios is non-trivial due to the inherent data sparsity and intent constraints compared to recommendation feeds. To this end, we propose SaFRO, a novel framework designed to optimize user satisfaction in short-video search. We first construct a satisfaction-aware reward model that utilizes query-level behavioral proxies to capture holistic user satisfaction beyond item-level interactions. Then we introduce Dual-Relative Policy Optimization (DRPO), an efficient policy learning method that updates the fusion policy through relative preference comparisons within groups and across batches. Furthermore, we design a Task-Relation-Aware Fusion module to explicitly model the interdependencies among different objectives, enabling context-sensitive weight adaptation. Extensive offline evaluations and large-scale online A/B tests on Kuaishou short-video search platform demonstrate that SaFRO significantly outperforms state-of-the-art baselines, delivering substantial gains in both short-term ranking quality and long-term user retention.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.IR'/>\n <published>2026-03-20T02:57:50Z</published>\n <arxiv:comment>9 pages, 8 figures</arxiv:comment>\n <arxiv:primary_category term='cs.IR'/>\n <author>\n <name>Renzhe Zhou</name>\n </author>\n <author>\n <name>Songyang Li</name>\n </author>\n <author>\n <name>Feiran Zhu</name>\n </author>\n <author>\n <name>Chenglei Dai</name>\n </author>\n <author>\n <name>Yi Zhang</name>\n </author>\n <author>\n <name>Yi Wang</name>\n </author>\n <author>\n <name>Jingwei Zhuo</name>\n </author>\n </entry>"
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