Gender Bias in Large Language Model Brand Recommendations: A Three-Study Analysis of Prompt-Induced Disparities Across Seasonal and Recipient Contexts

Large language models (LLMs) are increasingly used by consumers for product recommendations, yet their potential for perpetuating gender bias in commercial contexts remains understudied. We present a three-study analysis of gender-based disparities in brand recommendations across three major LLMs: Google Gemini 3 Flash, OpenAI GPT-5.2, and xAI Grok-4-1. Study 1 (\((n = 299)\) queries, December 2025) examined adult gift recipients (husband/wife/partner); Study 2 (\((n = 480)\) queries, January 2026) examined child recipients (son/daughter/child); Study 3 (\((n = 500)\) queries, February 2026) examined romantic-occasion recipients (husband/wife/boyfriend/girlfriend/partner) in a Valentine's Day context. Combined analysis of 1,279 queries reveals that gender bias is not only systematic but context-dependent : Christmas-framed queries produce 28--61% fewer brands for female-targeted prompts, while Valentine's Day framing reverses this pattern, with female-framed queries receiving 8.8% more brands from Gemini. Chi-square analysis confirms systematic gender-category associations across all three studies (Study 1: \((\chi^2 = 137.32)\); Study 2: \((\chi^2 = 524.32)\); both \((p < 0.001)\); Cram\'{e}r's \((V = 0.23)\)--\((0.38)\)). We identify ``category gatekeeping'' as the underlying bias mechanism, document 69 gender-locked brands across all studies, and introduce the Prompt-Adjusted Share of Recommendation (PASOR) metric with empirical validation. Cross-model agreement remains consistently low (Jaccard \((0.00)\)--\((0.68)\)). These findings establish that gender bias in LLM recommendations is systematic, platform-dependent, and modulated by seasonal context, with implications for AI fairness, open-world machine learning, and data-driven consumer decision-making.