Lidl shoppers say they'll miss monthly freebies. Can bonus points win them over? For 10 years, Lizi Hall has been doing most of her shopping at Lidl - and she's learned how to get the best value from its rewards scheme. We've got it down to a bit of an art, Lizi says. The loyalty system for me really did work.

Maryland is set to become the first state to ban surveillance pricing in grocery stores after Gov. Wes Moore said he will sign the new law taking effect October 2026.

Instacart settles Federal Trade Commission's claim it deceived US shoppers Instacart has agreed to pay $60m in refunds to settle allegations brought by the United States Federal Trade Commission (FTC) that the online grocery delivery platform deceived consumers about its membership programme and free delivery offers. According to court documents filed in San Francisco on Thursday, Instacart's offer of "free delivery" for first orders was illusory because shoppers were charged other fees, the FTC alleged. "The FTC is focused on monitoring online delivery services to ensure that competitors are transparently competing on price and delivery terms," said Christopher Mufarrige, who leads the FTC's consumer protection work. An Instacart spokesperson said the company flatly denies any allegations of wrongdoing, but that the settlement allows the company to focus on shoppers and retailers. "We provide straightforward marketing, transparent pricing and fees, clear terms, easy cancellation, and generous refund policies -- all in full compliance with the law and exceeding industry norms," the spokesperson said.

AI helps drive record $11.8 billion in Black Friday online spending People line up outside of a store during Black Friday in Woodbury, New York, on Friday. Although some shoppers queued up, many others used AI-powered shopping tools to help drive a surge in online spending on Black Friday. AI-powered shopping tools helped drive a surge in U.S. online spending on Black Friday, as shoppers bypassed crowded stores and turned to chatbots to compare prices and secure discounts amid concerns about tariff-driven price hikes. U.S. shoppers spent a record $11.8 billion online, up 9.1% from 2024 on the year's biggest shopping day, according to Adobe Analytics, which tracks 1 trillion visits that shoppers make to online retail websites. The holiday shopping season arrives amid tighter budgets, unemployment nearing a four-year high, U.S. consumer confidence sagging to a seven-month low and price tags that have shoppers watching every dollar. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

How to make sure you're getting a good deal on Black Friday Whether you're excited for the seasonal sales or avoiding the shops altogether, it's hard to escape the countless emails and social media adverts on Black Friday deals. The US holiday - which falls this Friday - has been firmly adopted by UK retailers, and what was once a single day of sales now spans the weeks before and after. However eight in 10 deals promoted during this buying bonanza were cheaper or the same price outside of the four-week Black Friday period, according to research from consumer group Which? This suggests shoppers could get the same or a better deal at other times of the year. But if you're planning to buy now, here's how to make sure you bag a bargain.

This study investigates the privacy risks associated with diffusion-based synthetic tabular data generation methods, focusing on their susceptibility to Membership Inference Attacks (MIAs). We examine two recent models, TabDDPM and TabSyn, by developing query-based MIAs based on the step-wise error comparison method. Our findings reveal that TabDDPM is more vulnerable to these attacks. TabSyn exhibits resilience against our attack models. Our work underscores the importance of evaluating the privacy implications of diffusion models and encourages further research into robust privacy-preserving mechanisms for synthetic data generation.

Conversational recommender systems promise rich interactions for e-commerce, but balancing exploration (clarifying user needs) and exploitation (making recommendations) remains challenging, especially when deploying large language models (LLMs) with vast product catalogs. We address this challenge by modeling the breadth of user interest via the entropy of retrieval score distributions. Our method uses a neural retriever to fetch relevant items for a user query and computes the entropy of the re-ranked scores to dynamically route the dialogue policy: low-entropy (specific) queries trigger direct recommendations, whereas high-entropy (ambiguous) queries prompt exploratory questions. This simple yet effective strategy allows an LLM-driven agent to remain aware of an arbitrarily large catalog in real-time without bloating its context window.

This paper examines the use of in-store customers as delivery couriers in a centralized crowd-shipping system, targeting the growing need for efficient last-mile delivery in urban areas. We consider a brick-and-mortar retail setting where shoppers are offered compensation to deliver time-sensitive online orders. To manage this process, we propose a Markov Decision Process (MDP) model that captures key uncertainties, including the stochastic arrival of orders and crowd-shippers, and the probabilistic acceptance of delivery offers. Our solution approach integrates Neural Approximate Dynamic Programming (NeurADP) for adaptive order-to-shopper assignment with a Deep Double Q-Network (DDQN) for dynamic pricing. This joint optimization strategy enables multi-drop routing and accounts for offer acceptance uncertainty, aligning more closely with real-world operations. Experimental results demonstrate that the integrated NeurADP + DDQN policy achieves notable improvements in delivery cost efficiency, with up to 6.7\% savings over NeurADP with fixed pricing and approximately 18\% over myopic baselines. We also show that allowing flexible delivery delays and enabling multi-destination routing further reduces operational costs by 8\% and 17\%, respectively. These findings underscore the advantages of dynamic, forward-looking policies in crowd-shipping systems and offer practical guidance for urban logistics operators.

Sorry to tell you this, but Google's new AI shopping tool appears eager to give J. D. Vance breasts. This week, at its annual software conference, Google released an AI tool called Try It On, which acts as a virtual dressing room: Upload images of yourself while shopping for clothes online, and Google will show you what you might look like in a selected garment. Curious to play around with the tool, we began uploading images of famous men--Vance, Sam Altman, Abraham Lincoln, Michelangelo's David, Pope Leo XIV--and dressed them in linen shirts and three-piece suits. But when we tested a number of articles designed for women on these famous men, the tool quickly adapted: Whether it was a mesh shirt, a low-cut top, or even just a T-shirt, Google's AI rapidly spun up images of the vice president, the CEO of OpenAI, and the vicar of Christ with breasts. It's not just men: When we uploaded images of women, the tool repeatedly enhanced their décolletage or added breasts that were not visible in the original images.

-- Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant gaps preventing state-of-the-art mobile manipulators from widespread real-world deployments, including their ability to reliably grasp items in unstructured environments. T o help bridge this gap, we developed SHOPPER, a mobile manipulation robot platform designed to push the boundaries of reliable and generalizable grasp strategies. We develop these grasp strategies and deploy them in a real-world grocery store - an exceptionally challenging setting chosen for its vast diversity of manipulable items, fixtures, and layouts. Additionally, we provide an in-depth analysis of our latest real-world field test, discussing key findings related to fundamental failure modes over hundreds of distinct pick attempts. Through our detailed analysis, we aim to offer valuable practical insights and identify key grasping challenges, which can guide the robotics community towards pressing open problems in the field. I. INTRODUCTION Grasping and placing of a large diversity of novel items is a fundamental problem in mobile manipulation, necessary for robots to be useful in real-world settings like the home. Significant progress has been made over the past decade, showing mobile manipulators grasping a diversity of items in lab settings. However, many grasping works abstract away different parts of the robot stack, leading to assumptions that do not hold in the real-world (e.g. Furthermore, few works have (1) been able to make the jump to the real world, or (2) exhibited reliability close to necessary for real-world deployment. This is reflected in the dearth in widespread deployments of commercial mobile manipulators.