Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines

תקציר מקורי באנגליתarXiv:2503.08051v2 Announce Type: replace Abstract: In the era of generative AI, recommender systems are moving from precise prediction to trustworthy generation. Large language models (LLMs) support this shift by inferring user interests and producing natural-language explanations. However, LLM-based recommendation suffers from a fundamental obstacle: popularity bias. Through pre-training on massive corpora, LLMs tend to rely on global statistics and trend signals, yielding recommendations that follow popularity rather than genuine preference. As this bias is entangled in model parameters and is hard to remove directly, we propose Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines (NPRec), a model-agnostic framework that mitigates popularity b