We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category-attribute pairs. Starting from a seed of human-crafted prompts, the cascade progressively optimizes instructions to meet catalog-specific requirements. This approach bridges the gap between general language understanding and domain-specific knowledge at scale in complex industrial catalogs. Our extensive empirical evaluations shows the auto-prompt cascade improves precision and recall by $8-10\%$ over traditional chain-of-thought prompting. Notably, it achieves these gains while reducing domain expert effort from 5.1 hours to 3 minutes per attribute - a $99\%$ reduction. Additionally, the cascade generalizes effectively across five languages and multiple quality assessment tasks, consistently maintaining performance gains.

Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hindered by heterogenous lifecycle data and data silos. Current tools like Environmental Product Declarations (EPDs) and Digital Product Passports (DPPs) are limited by their dependency on seamless data integration and interoperability which remain significant challenges. To address these, we present the Circular Construction Product Ontology (CCPO), an applied framework designed to overcome semantic and data heterogeneity challenges in EoL decision-making for construction products. CCPO standardises vocabulary and facilitates data integration across supply chain stakeholders enabling lifecycle assessments (LCA) and robust decision-making. By aggregating disparate data into a unified product provenance, CCPO enables automated EoL recommendations through customisable SWRL rules aligned with European standards and stakeholder-specific circularity SLAs, demonstrating its scalability and integration capabilities. The adopted circular product scenario depicts CCPO's application while competency question evaluations show its superior performance in generating accurate EoL suggestions highlighting its potential to greatly improve decision-making in circular supply chains and its applicability in real-world construction environments.

Product attribute-value identification (PAVI) has been studied to link products on e-commerce sites with their attribute values (e.g., ) using product text as clues. Technical demands from real-world e-commerce platforms require PAVI methods to handle unseen values, multi-attribute values, and canonicalized values, which are only partly addressed in existing extraction- and classification-based approaches. Motivated by this, we explore a generative approach to the PAVI task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Since the attribute value pairs are unordered set elements, how to linearize them will matter; we, thus, explore methods of composing an attribute-value pair and ordering the pairs for the task. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods on large-scale real-world datasets meant for those methods.

When Svetlana Kordumova was studying for her doctorate in AI and computer vision, she grew frustrated by the process of looking for items to buy online. Search results were often inaccurate, and she knew the tech she was learning could improve the experience. Pixyle AI was launched in 2019 to improve product discovery on e-commerce sites and today announced a €1 million seed round (about $1.05 million USD) from South Central Ventures. The startup, which has offices in Amsterdam and North Macedonia, now works with over 20 clients, including Depop, Otrium and Minto. Over the past three years, it has tagged more than 250 million images and says its increased conversions for its retail customers by 10% on average.

There is a revolution in how marketers are using artificial intelligence (AI) and machine learning (ML) to help execute intelligent strategies and campaigns at scale. One important area where AI and ML can be put to good use is in market data management. "This is basically turning AI and ML into a useful tool for marketing itself," said Theresa Kushner, head of North American Innovation Center, NTT DATA Services, at The MarTech Conference. In this way, businesses can better understand all the data streaming in that relates to what's being done in markets, including who is buying products and other important buying trends. "AI and ML can help you sort through, organize that information and present it to you in a way that makes it more digestible within your marketing program," Kushner said.

The global product information management (PIM) market is estimated to reach $16.5 billion by 2026 from $9.9 billion in 2021. A major contributor to this growth lies in the integration of artificial intelligence (AI) and machine learning (ML) in PIM systems. This combination equips businesses with abilities that range from organising and analysing to translating product data for actionable inputs that can be exercised across channels. It also eases the integration with an intelligent system capable of actioning future data management tasks through knowledge input. Today's modern PIM solutions not only allow enterprises to centralise large volumes of data but also helps improve the quality of information to make it publish-worthy across channels.

Much of a professional communicator's work depends on tech that helps companies communicate with their stakeholders, create awareness and drive conversions among consumers, and track the results of their efforts. While every industry is bound to be affected by technology trends that continue to emerge in the new year, those working in the field of professional communications stand to feel the impact of recent technological advances, developments and use cases more than others. The experts of Forbes Communications Council have their fingers on the pulse of the latest movements in comms-related tech, and their insights can help communications and marketing teams make smart choices about which tools and resources to invest in and leverage in 2022. Here, 11 of them offer their best predictions as to which technologies will most impact professional communications in the coming year. Forbes Communications Council members share tech trends that will impact professional communications in 2022.

This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.

American innovation is slowing down. As the Center for Strategic and International Studies reports, "Whether measured in terms of triadic patents (patents filed in the United States, Europe, and Japan), most available measures of productivity, or even startup company creation, the United States' trademark innovative spirit has been gradually dampening for decades." AI can help resolve these issues and bring back America's waning innovation. In the recent NSCAI report, the committee recommends specific actions to the president and Congress, including supporting semiconductors and other strategic industries. The report missed one key area: AI-enabled commerce.

In today's competitive markets, B2B companies must constantly look for ways to go above and beyond what their peers have to offer. Many are using AI in B2B e-commerce to leap ahead. In fact, AI is fast becoming critical for business survival. Ritu Jyoti, program vice president, artificial intelligence at IDC, says, "companies will adopt AI -- not just because they can, but because they must." According to IDC, global spending on AI is predicted to double within the next four years, growing from $50.1 billion in 2020 to a forecasted $110 billion in 2024.