Site selection determines optimal locations for new stores, which is of crucial importance to business success. Especially, the wide application of artificial intelligence with multi-source urban data makes intelligent site selection promising. However, existing data-driven methods heavily rely on feature engineering, facing the issues of business generalization and complex relationship modeling. To get rid of the dilemma, in this work, we borrow ideas from knowledge graph (KG), and propose a knowledge-driven model for site selection, short for KnowSite. Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured. Based on UrbanKG, we employ pre-training techniques for semantic representations, which are fed into an encoder-decoder structure for site decisions. With multi-relational message passing and relation path-based attention mechanism developed, KnowSite successfully reveals the relationship between various businesses and site selection criteria. Extensive experiments on two datasets demonstrate that KnowSite outperforms representative baselines with both effectiveness and explainability achieved.

It's a perfect time to start focusing a little more on self-care. As beautiful fall turns into winter, we tend to hunker down in our houses, getting less sun and fresh air and spending more time under the covers. But between scares, you might want to browse these deals on gadgets that should help make you feel good all around. Special offer for Gear readers: Get a 1-year Subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day.

At AskSid, we consider ourselves intelligent experience engineers, collaborating with global retail brands to elevate the level of conversations, and supporting brands beyond just customer support. The core of AskSid's offerings lies in the retail AI brain we build for brands, an intelligence layer that supersedes all customer communication, driving intuitive support, and crafting simplified yet impactful shopping journeys. This is the driving force behind our insights engine, allowing brands to dig deep into conversational data and seek out hidden cues, resulting in a more complex and deep understanding of every single customer. AskSid's domain expertise also extends to mapping extracted insights to business opportunities, leading brands to discover previously unknown possibilities, including demand in varied markets and geographies, targeted marketing campaigns, new product lines, and well-mapped shopping journeys. AskSid has live implementations in 23 countries and supports 100 international languages.

Artificial intelligence will play a significant role in your Christmas online shopping adventures. That's because AI is being used by 81 percent of online merchants to improve sales ahead of the holiday season. These companies, according to VentureBeat, have expanded their artificial intelligence spending to do precisely that. The companies in question are categorised as small to medium-sized businesses that may not be able to compete with online shopping behemoths like Amazon. Businesses are using artificial intelligence to help manage their supply chains (which are already overburdened owing to the pandemic), guard against ecommerce fraud, and even enhance overall sales.

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of \textbf{TargetUM} is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.

As we all know supply chain disruptions over the past two years do not seem to be going away any time soon. However, businesses are turning to new artificial intelligence-powered (AI) simulations, known as "digital twins," to help get products and services to consumers on time – especially as we head into the holiday shopping season. These digital twins can predict disruptions that lie ahead and suggest what to do about them. Digital twins are used to solve breakages in the supply chain by anticipating them before they happen and then using AI to devise a workaround. The term "digital twins" is meant to elicit the idea of simulating a complex system in a computer, creating a sort of "twin" that mirrors real-world objects (from shipping ports to products), and the processes of which they are a part.

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost never the end of the work, but is a part of a larger application or workflow. Another challenge is the varied nature of ML development activities performed by different user roles. For example, the DevOps engineer develops infrastructure components, such as CI/CD automation, builds production inference pipelines, and configures security and networking.

SageMaker Projects give organizations the ability to easily setup and standardize developer environments for data scientists and CI/CD systems for MLOps Engineers. With SageMaker Projects, MLOps engineers or organization admins can define templates which bootstrap the ML Workflow with source version control, automated ML Pipelines, and a set of code to quickly start iterating over ML use cases. With Projects, dependency management, code repository management, build reproducibility, artifact sharing and management become easy for organizations to set up. SageMaker Projects are provisioned using AWS Service Catalog products. Project templates are used by organizations to provision Projects for each of their users.

Algorithms now determine how much things cost. It's called dynamic pricing and it adjusts according to current market conditions in order to increase profits. The rise of e-commerce has propelled pricing algorithms into an everyday occurrence--whether you're shopping on Amazon, booking a flight, hotel or ordering an Uber. In this continuation of our series on automation and your wallet, we explore what happens when a machine determines the price you pay. This episode was reported by Anthony Green and produced by Jennifer Strong and Emma Cillekens. We're edited by Mat Honan and our mix engineer is Garret Lang, with sound design and music by Jacob Gorski. Jennifer: Alright so I'm in an airport just outside New York City and just looking at the departures board here seeing all these flights going different places… It makes me think about how we decide how much something should cost… like a ticket for one of these flights. Because where the plane is going is just part of the puzzle. The price of airfare is highly personalized.

Artificial intelligence will be playing a big part in your online shopping escapades for the holidays. That's because roughly 81% of online retailers are tapping AI to boost their sales in time for the holiday rush. According to VentureBeat, these businesses have increased their artificial intelligence budgets to do just that. The businesses in question are classified as small to medium enterprises who might not be competitive with other online shopping giants like Amazon. With artificial intelligence, the businesses are tapping into its power to help manage their supply chain (which is already stretched thin due to the pandemic), protect against ecommerce fraud, and even increase their overall sales.