After OpenAI's Instant Checkout feature fell short, Walmart is instead embedding its Sparky chatbot directly into ChatGPT and Google Gemini. Since November, Walmart has let some ChatGPT users order a limited selection of products without ever leaving OpenAI's chatbot interface. Sales have been disappointing, a Walmart executive vice president exclusively tells WIRED. The results suggest that a future where chatbots and AI agents take over ecommerce is still a way off, if it ever materializes. Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT.

Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Criminals are increasingly exploiting AI technology to take over people's mobile, banking and online shopping accounts, the UK's leading anti-fraud body has warned. Last year, a record number of scams were reported to the national fraud database, fuelled by AI, which allows for large-scale deception on "industrialised" levels, according to Cifas, the fraud prevention organisation. Its report showed 444,000 cases of fraud were reported by its members last year - a 6% increase on 2024.

We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL achieves classification accuracy competitive with established machine learning models without sacrificing interpretability. Further, we show that ECSEL satisfies some desirable properties regarding global feature behavior, decision-boundary analysis, and local feature attributions. Experiments on benchmark datasets and two real-world case studies i.e., e-commerce and fraud detection, demonstrate that the learned equations expose dataset biases, support counterfactual reasoning, and yield actionable insights.

Recommender systems have become ubiquitous across a wide range of fields, such as ecommerce, media consumption (including movies, books, music, news, etc.), social networks, finance, and many others, due to their effectiveness in identifying relevant items or content among numerous choices [1, 2]. Traditionally, recommender systems, largely based on collaborative filtering techniques, have focused on recommending individual (or "atomic") items, such as movies or books, by understanding users' preferences for these individual items. However, in certain application domains, recommending "composite" items (i.e., combinations of atomic items) represents a very important capability. For illustration, consider a clothing/fashion recommender system, where we want to recommend "outfits" - combinations of tops (t-shirts, shirts, sweaters) and bottoms (pants, skirts, shorts) - to users. In such a case, multiple fashion items in a recommended outfit ideally have to match both functionally and stylistically, which may require domain expertise (e.g., on things like style compatibility) beyond individual preferences. Another key challenge for such recommender systems is that a given user's personal preference for a composite item may not directly translate to the user's personal preferences for the underlying atomic items and vice versa.

Google introduces a new'Checkout' feature in Gemini and Search AI Mode, allowing US users to make direct purchases through conversations with AI. PCWorld reports that Google launched the Universal Commerce Protocol alongside major retailers like Shopify, Etsy, Walmart, and Target for seamless integration. Users can complete transactions using Google Pay or PayPal, streamlining online shopping by eliminating the need to navigate to separate websites. Google is launching a "Checkout" feature in its Gemini AI chatbot as well as in Google Search's AI Mode, according to a recent blog post . The feature allows users to purchase products without leaving the chat or search interface. Purchases can be completed with Google Pay or PayPal. At the same time, Google is also unveiling its Universal Commerce Protocol (UCP). This is an open standard that enables different AI agents, payment systems, and shops to work seamlessly together.

The Universal Commerce Protocol introduces three new major AI features meant to reduce friction when online shopping. To further push the limits of consumerism, Google has launched a new open standard for agentic commerce that's called Universal Commerce Protocol (UCP). In brief, it's a framework that combines the power of AI agents and online shopping platforms to help customers buy more things. Thanks to the introduction of UCP, Google is offering three new online shopping features. To start, Google's AI mode will have a new checkout feature that allows customers to buy eligible products from certain US retailers within Google Search.

Most existing benchmarks for grounding language in interactive environments either lack realistic linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. We develop WebShop - a simulated e-commerce website environment with 1.18 million real-world products and 12,087 crowd-sourced text instructions. In this environment, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase a product given an instruction. WebShop provides several challenges including understanding compositional instructions, query (re-)formulation, dealing with noisy text in webpages, and performing strategic exploration. We collect over 1,600 human trajectories to first validate the benchmark, then train and evaluate a diverse range of agents using reinforcement learning, imitation learning, and pre-trained image and language models. Our best model achieves a task success rate of 29%, which significantly outperforms rule heuristics but is far lower than expert human performance (59%). We also analyze agent and human trajectories and ablate various model components to provide insights for developing future agents with stronger language understanding and decision making abilities. Finally, we show our agent trained on WebShop exhibits non-trivial sim-to-real transfer when evaluated on amazon.com