A new roadmap paper outlines the evolution of recommender systems towards autonomous agents capable of reasoning, planning, and acting. It introduces a taxonomy based on autonomy levels and three core paradigms: agent-assisted recommendation, agent-as-recommender, and agent-as-user-simulator. The paper also discusses current evaluation methodologies, their limitations, and identifies open challenges in areas like lifelong user modeling, trustworthiness, and efficiency for developing more human-aligned recommendation agents. AI
IMPACT This roadmap could guide the development of more sophisticated and interactive recommender systems, enhancing user experience and personalization.
RANK_REASON The cluster contains a single academic paper detailing a new research roadmap.
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