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Survey maps autonomous AI agents in recommender systems

By PulseAugur Editorial · [1 sources] ·

This paper surveys the evolving field of autonomous information-seeking within recommender systems, driven by the integration of large language model-based agents. It introduces a taxonomy based on autonomy levels and three core paradigms: agent-assisted recommendation, agent-as-recommender, and agent-as-user-simulator. The survey also examines current evaluation methodologies, discusses their limitations, and outlines open challenges in areas such as lifelong user modeling, trustworthiness, and efficiency for developing more human-aligned recommendation agents. AI

IMPACT This survey provides a framework for understanding and developing more autonomous and human-aligned AI agents within recommender systems.

RANK_REASON The item is a survey paper published on arXiv, detailing research on AI agents in recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey maps autonomous AI agents in recommender systems

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The item is a survey paper published on arXiv, detailing research on AI agents in recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems

    The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive systems that can reason, plan, and act. This survey provides a comprehensive overview of this emerging lan…

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