aiqbench analysis
AgenticRec addresses the critical misalignment between tool-integrated reasoning trajectories and recommendation feedback in LLM-based agents, proposing a two-stage training paradigm to sharpen preference boundaries. By optimizing agentic capabilities through bidirectional preference reasoning over self-bootstrapped hard pairs, this framework offers significant improvements for personalized systems relying on implicit feedback. The work represents a substantive advancement in aligning generative agent behaviors with specific recommendation objectives rather than general-purpose chat tasks.
AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization
arXiv:2603.21613v2 Announce Type: replace-cross Abstract: Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at https://anonymous.4open.science/r/AgenticRec-FB16.
Abstract:Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at this https URL.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
arXiv:2603.21613 [cs.IR]
(or arXiv:2603.21613v2 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2603.21613 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 231 words