LLM recommendation systems show strong bias toward well-known brands that can be overcome by small rating advantages or authority marketing claims, creating a social dilemma when multiple brands optimize.
M., Buchholz, A., Schwöbel, P
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
A large-scale audit of AI commercial recommendations reveals tier-specific failure modes: L1 brands reach recommendations but convert at 25-41%, L2 convert highest at 37-52%, L3 is an inflection point, and L4/L5 brands suffer 48-52% complete invisibility.
AI brand mentions in ChatGPT/Claude/Gemini conversations causally raise open-web searches and visits by 4.3/2.4/1.0pp via pre-trend event study, stance classifier, and same-category controls on joined clickstream-conversation panel data.
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
Persona prefixes reduce brand recommendation Jaccard similarity by 0.12-0.20, with mid-market brands swapping up to 75% of recommendations while category leaders remain ~80% consistent across OpenAI and Anthropic models.
GraphRAG-IRL fuses graph-grounded MaxEnt IRL pre-ranking with persona-guided LLM re-ranking to deliver up to 16.8% NDCG@10 gains over IRL-only baselines on MovieLens and consistent 4-6% gains on KuaiRand.
citing papers explorer
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Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
LLM recommendation systems show strong bias toward well-known brands that can be overcome by small rating advantages or authority marketing claims, creating a social dilemma when multiple brands optimize.
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Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit
A large-scale audit of AI commercial recommendations reveals tier-specific failure modes: L1 brands reach recommendations but convert at 25-41%, L2 convert highest at 37-52%, L3 is an inflection point, and L4/L5 brands suffer 48-52% complete invisibility.
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From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web
AI brand mentions in ChatGPT/Claude/Gemini conversations causally raise open-web searches and visits by 4.3/2.4/1.0pp via pre-trend event study, stance classifier, and same-category controls on joined clickstream-conversation panel data.
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
Persona prefixes reduce brand recommendation Jaccard similarity by 0.12-0.20, with mid-market brands swapping up to 75% of recommendations while category leaders remain ~80% consistent across OpenAI and Anthropic models.
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GraphRAG-IRL: Personalized Recommendation with Graph-Grounded Inverse Reinforcement Learning and LLM Re-ranking
GraphRAG-IRL fuses graph-grounded MaxEnt IRL pre-ranking with persona-guided LLM re-ranking to deliver up to 16.8% NDCG@10 gains over IRL-only baselines on MovieLens and consistent 4-6% gains on KuaiRand.