AI RESEARCH
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
arXiv CS.AI
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ArXi:2606.02883v1 Announce Type: cross Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior. By controlling what we see, they shape what we perceive, raising concerns about filter bubbles, radicalization, polarization, and social inequality. Large language models (LLMs) enable powerful personalization, intensifying these dynamics. Yet most recommenders are tuned for engagement or limited accuracy metrics, with little attention to broader social implications, e.g.