Large Language Models (LLMs) have significantly advanced generative query recommendation. However, existing alignment methods primarily focus on standard chatbot scenarios, falling short in on-device intelligent assistants where users predominantly expect the rapid invocation of system-level tools. Moreover, directly aligning LLMs with real-world click logs introduces severe noise due to varying user activity levels and the failure to emphasize execution-oriented queries. To address these challenges, we propose ToolRec, a calibrated preference alignment framework tailored for on-device query recommendation. To ground query recommendation with executable actions, we first construct SysToolKit, a comprehensive repository of 708 system tools, paired with a context-aware tool retrieval mechanism to ensure recommendation relevance. We then propose a dual-level calibration mechanism to refine raw click data, effectively mitigating user behavioral noise by calibrating signals based on user activity levels, while simultaneously up-weighting click signals on system-level tool-invoking queries. Guided by these refined preference signals, we then align the model using a sample-level weighted Kahneman-Tversky Optimization (KTO). Extensive online A/B tests on our mobile assistant platform OPPO Xiaobu, which has over 150 million monthly active users, demonstrate that ToolRec can significantly improve Click-Through Rate (CTR) and total clicks volume over strong baselines while maintaining high query relevance.

翻译：摘要：大语言模型显著推进了生成式查询推荐的发展。然而，现有对齐方法主要聚焦于标准聊天机器人场景，在用户主要期望快速调用系统级工具的设备端智能助手中表现不足。此外，直接使用真实点击日志对齐大语言模型会因用户行为活跃度差异及未能凸显执行型查询而引入严重噪声。为解决上述挑战，我们提出ToolRec——一种专为设备端查询推荐设计的校准偏好对齐框架。为将查询推荐与可执行动作相结合，我们首先构建SysToolKit——包含708个系统工具的综合性知识库，并配以上下文感知的工具检索机制确保推荐相关性。随后提出双层级校准机制对原始点击数据进行优化：通过基于用户活跃度信号校准有效缓解用户行为噪声，同时提升系统级工具调用查询的点击信号权重。基于这些精炼的偏好信号，我们采用样本加权卡内曼-特沃斯基优化方法对齐模型。在月活用户超1.5亿的移动助手平台OPPO小布上开展的大规模在线A/B测试表明，ToolRec在保持高查询相关性的同时，相较于强基线方法显著提升了点击率与总点击量。