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ToolRec framework boosts on-device assistant query recommendations

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.

RANK_REASON The cluster contains a research paper detailing a new framework and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Lizhong Wang ·

    ToolRec: Calibrated Preference Alignment for Query Recommendation in On-Device Assistants

    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 …