With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.

With a quarter of people in the UK using AI to find products, marketers must not only appeal to shoppers directly but to AI bots and their opaque decision-making process. With a quarter of people in the UK using AI to find products, marketers must not only appeal to shoppers directly but to AI bots and their opaque decision-making process. 'What to buy Dad for Christmas': is retail ready for the AI shopping shift? Consumer test drive: can AI do your Xmas gift shopping for you? While traditional internet search, social media - especially TikTok and Instagram - and simply wandering a local high street will still be the main routes to presents for most this year, about a quarter of people in the UK are already using AI to find the right products, according to PricewaterhouseCoopers.

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The Best Razor I've Ever Used Is on Sale Henson Razors are engineered to give a spectacular shave with dirt-cheap generic blades. Razors are one of the most heavily and competitively marketed products in American capitalism. Made with steel and plastic that costs a few pennies, but sold for a thousand percent profit, the razor market is the subject of vigorous academic study and debate. The founder of Gillette famously came up with a model of basically giving away the razor handle so he could sell the blades. Canadian startup Henson has the opposite model, charging $79 for a razor that can give you an excellent shave with dirt-cheap disposable blades that cost about 15 cents each .

We've used all of these down comforter picks on our own beds and can attest they're excellent for winter (or year-round). Down comforter deals are usually easier to find during the warmest months of the year, but we've rustled up a few worth your while before it gets too bitterly cold outside. Down comforters are some of my very favorite products to test. I live in northern Illinois, and around this time of year it's usually either snowing, dangerously cold, or both. I have tried several blankets for our down comforter buying guide in my day-to-day life over the course of months.

Bring this to a holiday party and take pictures or wrap one up as a great gift for anyone who likes photos taken without a smartphone. We may earn revenue from the products available on this page and participate in affiliate programs. Did you know you're not supposed to shake a Polaroid picture? There's actually a chemical process going on inside of each Polaroid print and shaking it won't help it turn out any better. In fact, it might ruin the emulsion required for the picture to form.

'This pistol is something a bit different,' according to a firearms expert. Despite its generic appearance, this 18th century firearm features a comparatively unique design. Breakthroughs, discoveries, and DIY tips sent every weekday. It's difficult to resist raising an eyebrow at an Oklahoma robbery suspect's alleged recent weapon-of-choice . According to several Oklahoma news outlets including WKTUL, a 24-year-old man was arrested on December 6 by Tulsa police after allegedly robbing a liquor store using what employees described as an "old-timey musket."

Rachael Dunfell knew two things about her husband's 21-year-old cousin: that he liked specialised racing bikes and that he was interested in the Vikings. But those pieces of information yielded few ideas for a suitable Christmas gift. So Rachael, 33, from Manchester, turned to artificial intelligence. She inputted his age, his hobby and his interest into Copilot, the Microsoft-owned chatbot, which led her to the website of a niche retailer that sells Viking-themed metal bike parts. It's just something that I really would never have known existed, she said, but it was perfect.

Gear Outdoor Gear Amazon dropped Ororo's heated jackets, vests, and gloves to prices lower than Black Friday Stay warm all winter with big discounts on Ororo's most popular heated outerwear and accessories. We may earn revenue from the products available on this page and participate in affiliate programs. If you're staring down a few more months of cold commutes, dog walks, or pretending to have fun at an outdoor holiday market that charged you $18 for a hot chocolate, heated layers can make winter feel a lot more survivable. ORORO's battery-powered vests, jackets, and accessories hide built-in heating elements to warm your core (and even your hands and legs) at the press of a button, and a bunch of their most popular pieces are on sale at Amazon right now. Whether you want a low-profile heated vest to slip under a coat or a fully insulated jacket with multiple heat zones and dual controls, these discounts cover just about every style.

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.