SW AG: Item Recommendations using Convolutions on Weighted Graphs Amit Pande, Kai Ni and V enkataramani Kini Data Sciences, Target Corporation Abstract --Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SW AG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embed-dings for nodes (items) that incorporate both graph structure as well as node feature information such as item-descriptions and item-images. The three important SWAG operations that enable us to efficiently generate node embeddings based on graph structures are (a) Sampling of graph to homogeneous structure, (b) W eighting the sampling, walks and convolution operations, and (c) using AGgregation functions for generating convolutions. The work is an adaptation of graphSAGE over weighted graphs. We deploy SW AG at T arget and train it on a graph of more than 500K products sold online with over 50M edges. Offline and online evaluations reveal the benefit of using a graph-based approach and the benefits of weighing to produce high quality embeddings and product recommendations. I NTRODUCTION Convolutional Neural Networks (CNNs) are used to establish state-of-the-art performance on many Computer Vision applications [2]. CNNs consist of a series of parameterized convolutional layers operating locally (around neighboring pixels of an image) to obtain hierarchy of features about an image. The first layer learns simple edge-oriented detectors. Higher layers build up on the learning of lower layers to learn more complex features and objects. The success of CNNs in Computer Vision has inspired efforts to extend the convolu-tional operation from regular grids (2D images), to graph-structured data [9]. Graphs, such as social networks, word co-occurrence networks, guest purchasing behavior, protein-protein interactions and communication networks, occur naturally in various real-world applications. Analyzing them yields insights into the structure of society, language, and different patterns of communication.

Save on a Roomba ahead of the Black Friday shopping rush. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. The holidays are right around the corner, which means we'll all have to attend to our hosting duties for Friendsgivings, holiday parties, and fancy dinners. If you want your home looking spiffy for when the guests arrive, there's no better time to invest in a robot vacuum to keep your floors clean of dirt, pet hair, and dust bunnies.

Inc said on Tuesday it would use artificial intelligence (AI) technology from Amazon.com Inc's cloud computing unit to improve customer service apps. Salesforce makes software systems that businesses use to house customer information, like which products or services a customer has purchased and how long they have been a customer. Agents use the information to solve customer issues. Salesforce said it will use technology from Amazon Web Services to turn the customer's spoken words into text, where it can be translated into different languages or analyzed to determine whether the customer is angry or satisfied, all in real time.

Artificial intelligence (AI) is more than just cyborgs in movies trying to destroy humanity. It has actual real-world applications that can make our lives better and our businesses stronger and more profitable. In retail, artificial intelligence is being adopted rapidly - between 2016 and 2018 there was a 600% increase in adoption. Unfortunately the adoption rate is still relatively low, ranging from 26% for home improvement stores to 33% for apparel and footwear. If AI can make such a big difference, why isn't everyone adopting it?

I would like to start with the fact that the retail market today is developing by leaps and bounds, but traders by the way also do not standstill. On the contrary, they gradually seize the initiative by introducing Artificial Intelligence into their products. Note, when many things were simply impossible, but thanks to the arrival chatbots much have changed. Chatbots are one of the most effective tools in building relationships between customers. More and more retailers choose eCommerce chatbots as a primary method of communication with the visitors of their online stores. The main benefits of using Chatbot technology are reducing costs on the workforce and saving time.

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A decade ago, the British department-store chain John Lewis built itself a long warehouse, painted in gradations of sky blue. The shed, as it is called in the industry, cost £100m and covered 650,000 sq ft. Windsor Castle could easily fit inside it. John Lewis named the shed Magna Park 1, after the site where it stands: a "logistics campus" of warehouses, roads, shipping containers and truck bays east of Milton Keynes. Magna Park 1 was intended to supply the company's stores around southern England, but almost as soon as it was finished, John Lewis realised that it wasn't enough. The pace of e-commerce was flying, and Magna Park 1 opened in the midst of a spell in which, between 2006 and 2016, the share of John Lewis deliveries going direct to customers rose 12-fold. So John Lewis built Magna Park 2, measuring 675,000 sq ft. After that, the company realised it needed a new shed for Waitrose, its supermarket chain, where home deliveries were skyrocketing, too. "It became a bit of a standing joke," said Philip Stanway, a regional director at Chetwoods, the architecture firm that designed and built all these facilities. "They used to come to meetings with their forecasts, and they'd say: 'Screw this. This is the new forecast,'" Stanway said, making a scribbling motion in the manner of a John Lewis executive hastily updating the numbers. "We couldn't build the buildings quick enough for them."

As the digital world continues to modernise, and new technologies are developed, the AI and Data Strategy book simplifies the complexities and introduces a structured way to update your strategy to include AI and Data capabilities The book defines the technologies and why they are important and this includes Automatic cars, drones, robots, blockchain, data science, machine learning and internet of things. Most of us are unsure what this all means and may think it's a bit technical and over our head. In the end we really just want to manage our information more securely and be able to access it anywhere anytime any place. The book makes the connection between what the technology is and how it can help us achieve your goals in accessing information. The AI and Data strategy book will get you started on the what, how, who and why to enable you to build and implement an AI and Data strategy.

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It can reduce your labeling costs by up to 70% using automatic labeling. This blog post explains the Amazon SageMaker Ground Truth chaining feature with a few examples and its potential in labeling your datasets. Chaining reduces time and cost significantly as Amazon SageMaker Ground Truth determines the objects that are already labeled and optimizes the data for automated data labeling mode. As a prerequisite, you might want to check the post "Creating hierarchical label taxonomies using Amazon SageMaker Ground Truth" that shows how to achieve multi-step hierarchical labeling and the documentation on how to use the augmented manifest functionality.

Sainsbury's commercial and technology teams are working with Accenture to implement machine learning processes that they say are providing the retailer with better insight into consumer behaviour. Using the Google Cloud Platform (GCP), the key aim of the collaboration is to generate new insights on what consumers want and the trends driving their eating habits. By tapping into data from multiple structured and unstructured sources, the supermarket chain has developed predictive analytics models that it uses to adjust inventory based on the trends it spots. According to Alan Coad, managing director of Google Cloud in the UK and Ireland, the platform can "ingest, clean and classify that data", while a custom-built front-end interface for staff can be used "to seamlessly navigate through a variety of filters and categories" to generate the relevant insights. Phil Jordan, group CIO of Sainsbury's, said: "The grocery market continues to change rapidly. "We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them.