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arXiv · 2608.07989

PushDualGen: Enabling LLMs to Generate Semantic IDs with Interpretable Copy for Industrial Push Recommendation

Manjia Lin·Da Li·Yan Wang·Yong Jin·Zheming Ding·Wei Yuan·Lei Yan·Yanan Xia·Lu Zhang·Fan Yang·Xuanping Li·Yanan Niu
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Abstract

Push recommendation in KuaiShou proactively delivers personalized content to nearly one billion users to facilitate their engagement. Recently, generative recommendation has achieved end-to-end user personalization through semantic ID. However, their black- box characteristics make recommendation logics difficult to trace, hindering their deployment. OneRec-Thinking addresses this by incorporating CoT before generating SIDs, but this significantly increases inference cost. To support large-scale industrial applications, we propose PushDualGen, a lightweight generator, which first generates the SID and then produces a copy as a skippable explanation. PushDualGen has been deployed in Kuaishou's push recommendation system. Online A/B tests demonstrate the effectiveness of PushDualGen, delivering significant improvements in both user attraction and satisfaction. The effective play rate for videos recommended to users has relatively increased by 8.50%, while the dissatisfaction rate has relatively fallen by 37.70%. In the long term, PushDualGen optimises the content ecosystem, providing more exposure for long-tail videos.

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Manjia Lin, Da Li, Yan Wang, Yong Jin, Zheming Ding, Wei Yuan, Lei Yan, Yanan Xia, Lu Zhang, Fan Yang, Xuanping Li, Yanan Niu. 2026-08-08. PushDualGen: Enabling LLMs to Generate Semantic IDs with Interpretable Copy for Industrial Push Recommendation. https://arxiv.org/abs/2608.07989

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