Matching identical products present in multiple product feeds constitutes a crucial element of many tasks of e-commerce, such as comparing product offerings, dynamic price optimization, and selecting the assortment personalized for the client. It corresponds to the well-known machine learning task of entity matching, with its own specificity, like omnipresent unstructured data or inaccurate and inconsistent product descriptions. This paper aims to present a new philosophy to product matching utilizing a semi-supervised clustering approach. We study the properties of this method by experimenting with the IDEC algorithm on the real-world dataset using predominantly textual features and fuzzy string matching, with more standard approaches as a point of reference. Encouraging results show that unsupervised matching, enriched with a small annotated sample of product links, could be a possible alternative to the dominant supervised strategy, requiring extensive manual data labeling.

Customers all over the world want to see how the clothes fit them or not before purchasing. Therefore, customers by nature prefer brick-and-mortar clothes shopping so they can try on products before purchasing them. But after the Pandemic of COVID19 many sellers either shifted to online shopping or closed their fitting rooms which made the shopping process hesitant and doubtful. The fact that the clothes may not be suitable for their buyers after purchase led us to think about using new AI technologies to create an online platform or a virtual fitting room (VFR) in the form of a mobile application and a deployed model using a webpage that can be embedded later to any online store where they can try on any number of cloth items without physically trying them. Besides, it will save much searching time for their needs. Furthermore, it will reduce the crowding and headache in the physical shops by applying the same technology using a special type of mirror that will enable customers to try on faster. On the other hand, from business owners' perspective, this project will highly increase their online sales, besides, it will save the quality of the products by avoiding physical trials issues. The main approach used in this work is applying Generative Adversarial Networks (GANs) combined with image processing techniques to generate one output image from two input images which are the person image and the cloth image. This work achieved results that outperformed the state-of-the-art approaches found in literature.

Demand forecasting is a prominent business use case that allows retailers to optimize inventory planning, logistics, and core business decisions. One of the key challenges in demand forecasting is accounting for relationships and interactions between articles. Most modern forecasting approaches provide independent article-level predictions that do not consider the impact of related articles. Recent research has attempted addressing this challenge using Graph Neural Networks (GNNs) and showed promising results. This paper builds on previous research on GNNs and makes two contributions. First, we integrate a GNN encoder into a state-of-the-art DeepAR model. The combined model produces probabilistic forecasts, which are crucial for decision-making under uncertainty. Second, we propose to build graphs using article attribute similarity, which avoids reliance on a pre-defined graph structure. Experiments on three real-world datasets show that the proposed approach consistently outperforms non-graph benchmarks. We also show that our approach produces article embeddings that encode article similarity and demand dynamics and are useful for other downstream business tasks beyond forecasting.

Alphabet-owned Wing has been trying to make drone delivery an actual thing, but the relatively minuscule payload capacity of modern delivery aircraft has been a serious obstacle. The company just unveiled a new drone that's a step in the right direction. The new model can handle payloads of up to five pounds, which is twice as much as Wing's previous flagship drone. It can also travel up to 65 MPH, which is pretty darned fast. The onboard battery allows for a 12 mile round trip, which is in line with previous metrics, so that translates to an under six-minute delivery time. That certainly beats pizza delivery.

Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of jewelry. In this work, we propose an approach for jewelry recognition using computer vision techniques and image captioning, trying to simulate this expert human behavior of analyzing accessories. The proposed methodology consist on using different image captioning models to detect the jewels from an image and generate a natural language description of the accessory. Then, this description is also utilized to classify the accessories at different levels of detail. The generated caption includes details such as the type of jewel, color, material, and design. To demonstrate the effectiveness of the proposed method in accurately recognizing different types of jewels, a dataset consisting of images of accessories belonging to jewelry stores in C\'ordoba (Spain) has been created. After testing the different image captioning architectures designed, the final model achieves a captioning accuracy of 95\%. The proposed methodology has the potential to be used in various applications such as jewelry e-commerce, inventory management or automatic jewels recognition to analyze people's tastes and social status.

When Walmart announced it would be holding a CES keynote for the first time, we were admittedly a little skeptical. Now it all makes sense, though: America's largest retailer came to CES 2024 in Las Vegas to talk about AI. In a joint announcement on Tuesday, the company said that it's teaming up with Microsoft to build what it bills as AI-powered shopping experiences. In his keynote, Walmart CEO Doug McMillon described how the integration of AI across its website and apps will be used to study shopper behavior and suggest future purchases. As you might expect, given Microsoft's involvement, the artificial intelligence underpinning these experiences will be powered by large language models made available through this partnership with Microsoft.

Walmart is expanding its drone delivery program from one pocket of the Dallas-Fort Worth area to millions of people in 30 municipalities in the area, Chief Executive Doug McMillon announced Tuesday at CES 2024. The retailer will use drone delivery systems operated by startup Zipline and by Alphabet subsidiary Wing, companies that have made hundreds of thousands of deliveries in recent years. They each recently obtained FAA clearance to fly their drones beyond visual line of sight (BVLOS) -- in other words, out of the eyesight of a human operator -- which makes large-scale drone delivery operations more practical and economical. Delivery drones offer fast service, with Walmart packages arriving between 10 and 30 minutes after an order is placed from stores up to 10 miles away. Walmart touts the technology for people who need missing cooking ingredients, last-minute birthday gifts, over-the-counter medications or movie night snacks.

Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.

Those who've been yearning for a chance to try the Apple Vision Pro headset and have the cash to spare won't need to wait much longer to snap one up. The company says the hotly anticipated device will arrive in the US on February 2. Pre-orders for the 3,499 mixed reality headset will open on January 19. The device will be available at all US Apple Store locations as well as through the company's web store. Those who require vision correction will need to snap up Zeiss optical inserts and attach them to the headset magnetically (Vision Pro doesn't work with glasses). Readers will cost 99, while prescription lenses will set you back 149.

Perplexity, a startup going after Google's dominant position in web search, has won backing from Jeff Bezos and venture capitalists betting that artificial intelligence will upend the way people find information online. Started less than two years ago, Perplexity has fewer than 40 employees and is based out of a San Francisco co-working space. The company's product, which it calls an answer engine, is used by about 10 million people monthly.