Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems

• URL: http://arxiv.org/abs/2606.17443v1
• Date: Tue, 16 Jun 2026 02:54:34 GMT
• Title: Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• Abstract summary: We study brand dynamics in skincare product search recommendations using large language models (LLMs)<n>We find that well-known brands get recommended 100% of the time when all products have the same specifications, but this dominance disappears with less than a +0.1-star rating advantage for a competitor.<n>Our results suggest that generative engine optimization (GEO) should be studied as an emerging marketing practice that shapes market competition.
• Score: 7.25871718341051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel. We study brand dynamics in LLM recommendations using skincare products -- a category where consumers cannot easily judge quality before buying and must rely on brand reputation -- across three commercial LLMs (GPT-4o-mini, Claude Sonnet, Gemini 3 Flash), with a robustness check on search goods. In three experiments, we find: (1) a Conditional Monopoly where well-known brands get recommended 100% of the time (IAI = 10.0) when all products have the same specifications, but this dominance disappears with less than a +0.1-star rating advantage for a competitor; (2) authority-style marketing language, including fabricated clinical-evidence claims, breaks this monopoly at a Bias Surplus Value equal to +0.17 rating points, with each model responding differently; and (3) a social dilemma in multi-brand GEO competition: when all brands adopt the same optimization strategy, individual payoff falls from +0.802 to +0.007 in our payoff proxy, and non-participating brands receive zero recommendations in our tests. Our results suggest that generative engine optimization (GEO) should be studied not only as a security risk, but also as an emerging marketing practice that shapes market competition.

Related papers

• Evaluating Brand Retrieval and Ranking in Large Language Model Recommendations [2.7506573311649762]
  Large language models (LLMs) are increasingly used for product recommendation.<n> evaluating their recommendations presents challenges that differ from conventional information retrieval and recommender systems.<n>We introduce a framework for evaluating open-ended LLM brand recommendations.
  arXiv  Detail & Related papers  (2026-09-14T20:09:12Z)
• Who Owns the AI Recommendation? A Multi-Industry Empirical Map of Brand Category Ownership Across Large Language Models [0.0]
  3,750 responses spanning 50 brands, five industries, and 250 brand-free category queries on three models.<n>We propose three exploratory metrics: the Category Ownership Index (COI), a brand's share of mentions within a category; the Competitive Vacuum Index (CVI), flagging categories with no single leader; and the Displacement Score (DS), quantifying asymmetric substitution between brand pairs.
  arXiv  Detail & Related papers  (2026-06-22T09:10:54Z)
• Creativity Benchmark: A benchmark for marketing creativity for large language models [0.509780930114934]
  Creativity Benchmark is an evaluation framework for large language models (LLMs) in marketing creativity.<n>The benchmark covers 100 brands (12 categories) and three prompt types (Insights, Ideas, Wild Ideas)
  arXiv  Detail & Related papers  (2025-09-05T04:44:29Z)
• SPARTA ALIGNMENT: Collectively Aligning Multiple Language Models through Combat [76.48873047003943]
  We propose SPARTA ALIGNMENT, an algorithm to collectively align multiple LLMs through competition and combat.<n>For each iteration, one instruction and two models are selected for a duel, the other models evaluate the two responses, and their evaluation scores are aggregated through a adapted elo-ranking based reputation system.<n>The peer-evaluated combat results then become preference pairs where the winning response is preferred over the losing one, and all models learn from these preferences at the end of each iteration.
  arXiv  Detail & Related papers  (2025-06-05T07:51:23Z)
• A Multifacet Hierarchical Sentiment-Topic Model with Application to Multi-Brand Online Review Analysis [6.661618396933143]
  The proposed method is built on a unified generative framework that explains review words with a hierarchical brand-associated topic model.<n>A novel hierarchical Polya urn (HPU) scheme is proposed to enhance the topic-word association among topic hierarchy.<n> Experimental studies demonstrate that the proposed method can be effective in detecting reasonable topic hierarchy and deriving accurate brand-associated rankings.
  arXiv  Detail & Related papers  (2025-02-26T08:30:06Z)
• The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility? [54.18519360412294]
  Large Language Models (LLMs) must balance between rejecting harmful requests for safety and accommodating legitimate ones for utility.<n>This paper presents a Direct Preference Optimization (DPO) based alignment framework that achieves better overall performance.<n>We analyze experimental results obtained from testing DeepSeek-R1 on our benchmark and reveal the critical ethical concerns raised by this highly acclaimed model.
  arXiv  Detail & Related papers  (2025-01-20T06:35:01Z)
• CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings [70.95565672516979]
  Existing benchmarks, like LiveCodeBench and USACO, fall short due to the unavailability of private test cases, lack of support for special judges, and misaligned execution environments.<n>CodeElo is a standardized competition-level code generation benchmark that effectively addresses all these challenges for the first time.
  arXiv  Detail & Related papers  (2025-01-02T13:49:00Z)
• "Global is Good, Local is Bad?": Understanding Brand Bias in LLMs [0.0]
  This research examines the biases exhibited by LLMs towards different brands. We find a consistent pattern of bias in this space -- both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries.
  arXiv  Detail & Related papers  (2024-06-20T04:52:19Z)
• MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures [57.886592207948844]
  We propose MixEval, a new paradigm for establishing efficient, gold-standard evaluation by strategically mixing off-the-shelf benchmarks. It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks.
  arXiv  Detail & Related papers  (2024-06-03T05:47:05Z)
• How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments [83.78240828340681]
  GAMA($gamma$)-Bench is a new framework for evaluating Large Language Models' Gaming Ability in Multi-Agent environments.<n>$gamma$-Bench includes eight classical game theory scenarios and a dynamic scoring scheme specially designed to assess LLMs' performance.<n>Our results indicate GPT-3.5 demonstrates strong robustness but limited generalizability, which can be enhanced using methods like Chain-of-Thought.
  arXiv  Detail & Related papers  (2024-03-18T14:04:47Z)
• Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity [55.29408396918968]
  We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification. Our contributions include both consistency and robustness by establishing top-$k$ consistency of LDR losses for multi-class classification. We propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance.
  arXiv  Detail & Related papers  (2021-12-30T00:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.