ToolRec: Calibrated Preference Alignment for Query Recommendation in On-Device Assistants
Researchers have developed ToolRec, a new framework designed to improve query recommendation in on-device intelligent assistants. This system addresses the limitations of existing methods by focusing on the rapid invocation of system tools, which is common in assistant usage. ToolRec utilizes a comprehensive repository of system tools and a dual-level calibration mechanism to refine raw user click data, reducing noise from varying activity levels and emphasizing tool-invoking queries. Extensive A/B testing on a platform with over 150 million monthly active users showed significant improvements in click-through rates and total clicks compared to existing baselines. AI
IMPACT Enhances on-device assistant utility by improving tool invocation accuracy and user engagement.