arxiv
PublishedOctober 31, 2026 at 4:00 AM
Statistical Detection of Positional Bias in LLM-Based Recommender Systems: Methodologies and Empirical Evidence
Publisher summary· verbatim
arXiv:2610.37962v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as recommendation generators in conversational recommender systems (RS) via retrieval-augmented generation (RAG). This flexible design introduces new sources of structural bias. This paper studies po
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSelf-Supervised Latent Action-Conditioned World Modeling for Time Series5harxivMerge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation5harxivMETATRON: A Promptable Whole-Body Execution Layer for Humanoids5harxivEstimating Implicit Expected Log Density Ratios5hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗