Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.

In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fine-tuning industry-specific ASR models, which significantly improved the performance of the fine-tuned ASR models in industry applications. Experimental results show that our method substantially improves the crucial auxiliary role of the ASR model in industry CRM systems, and this approach has also been adopted in actual industrial applications.

Wegmans is testing AI-powered Caper Carts at four New York locations, allowing shoppers to track spending in real time and skip checkout lines with automatic item detection technology.

For a while, dating apps seemed to make it easier, putting a city's worth of single people in the palm of your hand. But AI has cast a paranoid pall over what can already be a suboptimal experience. If you get a message that feels a little off, it is hard to know whether you are flirting with a bot--or just someone insecure enough to use ChatGPT as their own Cyrano de Bergerac. In frustration, my friend Lonni has started picking up women at the nail salon like it's 1997. Or, in the midst of an emotionally fraught conversation with a friend or family member, a text might read strangely.

The two-part special, 'The Amazon Review Killer,' is now streaming on Fox Nation. Amazon already dominates online shopping, but now it's setting its sights even higher. With a new artificial intelligence-powered project called Starfish, the company aims to become the world's most complete and trusted source of product information. The goal? Make every listing on Amazon accurate, detailed and easy to understand, whether the product is sold by Amazon or a third-party seller. If the project works as planned, it could save sellers hours of work and help shoppers find what they need faster.

Breakthroughs, discoveries, and DIY tips sent every weekday. Subway commuters in Shenzhen, China, may soon need to make room for a fleet of chunky, snack-carrying delivery robots. Earlier this week, more than three dozen autonomous, four-wheeled delivery robots boarded and exited active subway trains, and eventually delivered packages to several 7-Eleven convenience stores. Although this demonstration was only a preliminary test and took place during off-peak hours, the company behind the subway-riding robots believes they could soon help stock shelves at around 100 7-Eleven locations. The initiative is part of a broader effort in China and other countries to normalize the presence of delivery robots operating in public spaces.

Multi-horizon time series forecasting has many practical applications such as demand forecasting. Accurate demand prediction is critical to help make buying and inventory decisions for supply chain management of e-commerce and physical retailers, and such predictions are typically required for future horizons extending tens of weeks. This is especially challenging during high-stake sales events when demand peaks are particularly difficult to predict accurately. However, these events are important not only for managing supply chain operations but also for ensuring a seamless shopping experience for customers. To address this challenge, we propose Temporal-Aligned Transformer (TAT), a multi-horizon forecaster leveraging apriori-known context variables such as holiday and promotion events information for improving predictive performance. Our model consists of an encoder and decoder, both embedded with a novel Temporal Alignment Attention (TAA), designed to learn context-dependent alignment for peak demand forecasting. We conduct extensive empirical analysis on two large-scale proprietary datasets from a large e-commerce retailer. We demonstrate that TAT brings up to 30% accuracy improvement on peak demand forecasting while maintaining competitive overall performance compared to other state-of-the-art methods.

We consider a multi-retailer supply chain where each retailer can dynamically choose when to share information (e.g., local inventory levels or demand observations) with other retailers, incurring a communication cost for each sharing event. This flexible information exchange mechanism contrasts with fixed protocols such as always sharing or never sharing. We formulate a joint optimization of inventory control and communication strategies, aiming to balance the trade-off between communication overhead and operational performance (service levels, holding, and stockout costs). We adopt a common information framework and derive a centralized Partially Observable Markov Decision Process (POMDP) model for a supply chain coordinator. Solving this coordinator's POMDP via dynamic programming characterizes the structure of optimal policies, determining when retailers should communicate and how they should adjust orders based on available information. We show that, in this setting, retailers can often act optimally by sharing only limited summaries of their private data, reducing communication frequency without compromising performance. We also incorporate practical constraints on communication frequency and propose an approximate point-based POMDP solution method (PBVI/SARSOP) to address computational complexity. Numerical experiments on multi-retailer inventory scenarios demonstrate that our approach significantly improves the cost-service trade-off compared to static information sharing policies, effectively optimizing the schedule of information exchange for cooperative inventory control.

Accurately attributing user visits to specific Points of Interest (POIs) is a foundational task for mobility analytics, personalized services, marketing and urban planning. However, POI attribution remains challenging due to GPS inaccuracies, typically ranging from 2 to 20 meters in real-world settings, and the high spatial density of POIs in urban environments, where multiple venues can coexist within a small radius (e.g., over 50 POIs within a 100-meter radius in dense city centers). Relying on proximity is therefore often insufficient for determining which POI was actually visited. We introduce \textsf{POIFormer}, a novel Transformer-based framework for accurate and efficient POI attribution. Unlike prior approaches that rely on limited spatiotemporal, contextual, or behavioral features, \textsf{POIFormer} jointly models a rich set of signals, including spatial proximity, visit timing and duration, contextual features from POI semantics, and behavioral features from user mobility and aggregated crowd behavior patterns--using the Transformer's self-attention mechanism to jointly model complex interactions across these dimensions. By leveraging the Transformer to model a user's past and future visits (with the current visit masked) and incorporating crowd-level behavioral patterns through pre-computed KDEs, \textsf{POIFormer} enables accurate, efficient attribution in large, noisy mobility datasets. Its architecture supports generalization across diverse data sources and geographic contexts while avoiding reliance on hard-to-access or unavailable data layers, making it practical for real-world deployment. Extensive experiments on real-world mobility datasets demonstrate significant improvements over existing baselines, particularly in challenging real-world settings characterized by spatial noise and dense POI clustering.

Three major Japanese convenience store operators posted growth in their group operating revenues and profits in the March-May first quarter of the current business year, according to their earnings reports. Retail giant Seven & I Holdings, the operator of industry leader Seven-Eleven Japan, saw its mainstay overseas convenience store operations recover thanks to labor and other cost cuts. FamilyMart's operating profit grew 17.9% from a year before to 27.8 billion, as advertisements featuring Los Angeles Dodgers star Shohei Ohtani helped attract more customers and boost sales of onigiri rice balls. FamilyMart also attracted budget-minded consumers thanks to its discount sales of food items such as eggs and milk. As a result, the company's net profit jumped 36.7% to a record 21.1 billion.