AI 中文总结
针对生成式推荐模型黑箱化阻碍部署且推理成本高的问题，提出轻量模型PushDualGen，生成语义ID的同时生成可跳过的解释副本，已部署于快手推送系统，使有效播放率升8.50%、不满率降37.70%，优化了内容生态。
AI 中文摘要
快手的推送推荐系统主动向近10亿用户推送个性化内容以提升用户活跃度。近年来，生成式推荐通过语义ID实现了端到端的用户个性化，但因其黑箱特性导致推荐逻辑难以追踪，阻碍了部署。OneRec-Thinking通过在生成语义ID（SID）前引入思维链（CoT）解决该问题，但大幅增加了推理成本。为支持大规模工业应用，我们提出轻量生成模型PushDualGen，它先生成语义ID，再生成可跳过的解释副本。PushDualGen已部署于快手推送推荐系统，在线A/B测试显示其有效性：用户吸引力和满意度均显著提升，推荐视频的有效播放率相对提升8.50%，不满率相对下降37.70%。长期来看，PushDualGen优化了内容生态，为长尾视频提供更多曝光。
英文摘要
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.
