--Consumers often heavily rely on online product reviews, analyzing both quantitative ratings and textual descriptions to assess product quality. However, existing research hasn't adequately addressed how to systematically encourage the creation of comprehensive reviews that capture both customers sentiment and detailed product feature analysis. This paper presents CPR, a novel methodology that leverages the power of Large Language Models (LLMs) and T opic Modeling to guide users in crafting insightful and well-rounded reviews. Our approach employs a three-stage process: first, we present users with product-specific terms for rating; second, we generate targeted phrase suggestions based on these ratings; and third, we integrate user-written text through topic modeling, ensuring all key aspects are addressed. We evaluate CPR using text-to-text LLMs, comparing its performance against real-world customer reviews from Walmart. Our results demonstrate that CPR effectively identifies relevant product terms, even for new products lacking prior reviews, and provides sentiment-aligned phrase suggestions, saving users time and enhancing reviews quality. Quantitative analysis reveals a 12.3% improvement in BLEU score over baseline methods, further supported by manual evaluation of generated phrases. We conclude by discussing potential extensions and future research directions. I NTRODUCTION Product reviews play a crucial role for retailers, as they help build trust among potential customers by providing social proof. They influence purchase decisions [7], [9], [19], [25] by offering information on the quality and suitability of the product. Reviews also provide valuable feedback for retailers, allows them to improve their products and enhance customer satisfaction. Furthermore, product reviews contribute to product search optimization efforts [8], giving retailers a competitive advantage and fostering customer engagement and loyalty. Product review phrase suggestion is a sub-task of text-to-text generation in natural language processing (NLP). Online shopping is increasingly popular. However, customers often lack the motivation to write constructive reviews.

Even if you've already scrubbed the winter grime from your floors, Spring is undeniably the dirtiest season. Right now, Walmart has Ninja-Shark vacuums and floor cleaners at clearance prices, with some more than half off their original price. The sale includes stick vacuums, robot vacuums, and even steam mops for deep cleaning those hard surfaces. There are tons of models on sale, but the most popular ones will sell out soon enough, so don't hesitate if you see a rig that fits your home cleaning needs. Pet hair plays havoc with vacuum cleaners.

A fully automated warehouse system is changing the way we shop for groceries. Imagine a grocery store where your entire order is picked, packed and ready for delivery in just five minutes without a single human hand touching your food. This is exactly what's happening inside Ocado's revolutionary Hive, a fully automated warehouse system that's changing the way we shop for groceries. At the core of Ocado's Customer Fulfilment Centres, or CFCs, is The Hive, a massive 3D grid filled with thousands of grocery products. GET SECURITY ALERTS & EXPERT TECH TIPS -- SIGN UP FOR KURT'S THE CYBERGUY REPORT NOW Picture fleets of robots or "bots" zipping around at speeds up to about 9 miles per hour, all coordinated by an AI-powered "air traffic control" system that talks to each bot ten times every second.

-- 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.

Artificial intelligence gets tons of press – and for good reason. But AI's fast-rising expertise lies not just within the matrix of its own nifty algorithms, but also in its wider connections. It's about "intelligent connectivity" that relies on raw data – lots and lots of it – and on the communication networks that carry it. This blend of technologies may be surrounding you at a large store like Walmart. Retailers fight for their target audience using sensors galore, stationed in their aisles and checkout lines.

Crafting a marketing message (copy), or copywriting is a challenging generation task, as the copy must adhere to various constraints. Copy creation is inherently iterative for humans, starting with an initial draft followed by successive refinements. However, manual copy creation is time-consuming and expensive, resulting in only a few copies for each use case. This limitation restricts our ability to personalize content to customers. Contrary to the manual approach, LLMs can generate copies quickly, but the generated content does not consistently meet all the constraints on the first attempt (similar to humans). While recent studies have shown promise in improving constrained generation through iterative refinement, they have primarily addressed tasks with only a few simple constraints. Consequently, the effectiveness of iterative refinement for tasks such as copy generation, which involves many intricate constraints, remains unclear. To address this gap, we propose an LLM-based end-to-end framework for scalable copy generation using iterative refinement. To the best of our knowledge, this is the first study to address multiple challenging constraints simultaneously in copy generation. Examples of these constraints include length, topics, keywords, preferred lexical ordering, and tone of voice. We demonstrate the performance of our framework by creating copies for e-commerce banners for three different use cases of varying complexity. Our results show that iterative refinement increases the copy success rate by $16.25-35.91$% across use cases. Furthermore, the copies generated using our approach outperformed manually created content in multiple pilot studies using a multi-armed bandit framework. The winning copy improved the click-through rate by $38.5-45.21$%.

No matter who you are, the ability to manage documents efficiently while on the move is invaluable--an idea that is not lost on app developers. And right now, it is on sale, big time. For a limited time, new users can secure a lifetime subscription for just 24.99 (regularly 199.99) by using the code SCAN at checkout through April 27. The app allows you to digitize a variety of documents, including contracts, receipts, ID cards, books, and handwritten notes, directly from your iOS device. This eliminates the need for bulky scanning equipment and enables you to work or study from virtually anywhere.

They rise to 70ft (21 metres), tilt forward and zip away in different directions, each carrying a paper bag. On a sleepy morning in the Irish capital the takeoffs build to a steady one every few minutes, with barely anyone glancing at the constant stream of aircraft buzzing back and forth. "No one's looking up – no one ever looks up," says the man responsible, Bobby Healy, the founder of the Dublin startup Manna Aero. People probably should take notice, because the drones are part of an effort to realise an ambition shared by Amazon, the Google sister company Wing and the Californian startup Zipline: instant, autonomous home delivery. Healy and his big-tech rivals hope drone delivery will change the course of the retail industry across Ireland, and then into the UK as soon as this year.

Buying something in the shops used to be as simple as choosing the item and handing over the money. But in recent years, the great British shopping experience has dramatically changed. In 2025, artificial intelligence (AI) is patrolling Britain's retail stores to keep an eye on customers as they stock up on essentials. Now, people are subjected to a slew of AI-powered tech, including intelligent surveillance cameras, robots, facial recognition systems and online age checks. Home Bargains is the latest to follow the trend, with a new AI-enabled security system that watches you while you scan your own items.

And neither is easy to use when you're in a rush. So we, the StackCommerce deals team, tested an app that claims to do it all--scanning, signing, saving, and even faxing documents--right from your phone. After one week, we forgot any other methods existed. The SwiftScan document scanner and PDF editor app swept us off our feet. Plus, this weekend only, you can save an extra 18 on a lifetime subscription to the iOS and Android apps with code TAKE30 at checkout, dropping the price from 59.99 to 41.99.