Fraud is a multi-billion-dollar problem for today's retailers, but AWS machine learning can help weed out the problem. There are plenty of ways to take retailers out of pocket. As e-commerce continues to ramp up, so too does fraud. Each year, tens of billions of dollars are lost this way – and every time a solution is found, cybercriminals are just as quick to find ways to circumvent them. Even in 2020, countless techniques are successfully in operation.

Customers are provided with a variety of options to choose from. The planning, marketing strategy, and execution for such sales require a year-long process. However, the most daunting task relies on the retailer, i.e. to understand customers' demand and preferences. A plethora of data regarding the demands of the consumers and customers' feedback is available to the retailers. However, due to the complexity of the'mixed-data' analysis with limited technology, often the partial picture of customers' feedback is presented to the retailer. This becomes difficult to understand the pattern in which the customer demand works.

Alternative data and machine learning are about to become essential components of the modern investment process. This excellent book offers practitioners a rich collection of case studies written by some of the most capable quants in the world today. It will be on our shelves here at Quandl for sure.

Retailers around the globe are cautiously reopening their doors after facing perhaps the most challenging times in decades, while in some areas of the world the toughest times may still lie ahead. The short-term disruption caused by the COVID-19 pandemic is clear, but its impact on the future of retail operations is yet to be seen. This uncertainty is creating unseen challenges for retailers, who are looking to make smart investments in technology that will not become obsolete once health and safety guidelines and restrictions lift. Many retailers have concentrated on implementing technologies primarily to mitigate risk from the ongoing pandemic. However, investing in a new breed of smart surveillance cameras today enables retailers to design a sustainable system with robust application offerings for multiple types of scenarios, both during the pandemic and as operations begin to normalize.

A chatbot can benefit the biggest or the smallest eCommerce company. Whether you're selling clothes, shoes, makeup, electronics, art & crafts, or furniture, having a chatbot is a great investment for your store. Big brands benefit from chatbots because it allows them to engage with numerous customers in a timely and efficient manner. At the same time, small stores that cannot hire several employees can compete with bigger businesses by providing immediate service. Having a chatbot can help increase your revenue because it solves different customer problems that often lead to cart abandonment.

While the whole planet was frozen by the coronavirus pandemic, offline stores found they couldn't compete with even the smallest online stores when people's lifestyles were limited by their homes or neighborhoods. But those who have just started online sales this year will quickly find out what to do to sell efficiently on the internet. This is why the overall competition will rise. Wondering how you can gain a foothold at this moment? Take a look at modern technologies – artificial intelligence (AI), machine learning (ML), and big data analysis.

The E-commerce platform has become the principal battleground where people search, browse and pay for whatever they want. Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia. In this paper, we propose a novel user intent prediction model, TPG-DNN, to complete the challenging task, which is based on adaptive gated recurrent unit (GRU) loss function with multi-task learning. We creatively use the GRU structure and total probability formula as the loss function to model the users' whole online purchase process. Besides, the multi-task weight adjustment mechanism can make the final loss function dynamically adjust the importance between different tasks through data variance. According to the test result of experiments conducted on Taobao daily and promotion data sets, the proposed model performs much better than existing click through rate (CTR) models. At present, the proposed user intent prediction model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform, which greatly improve the user experience and shopping efficiency, and benefit the gross merchandise volume (GMV) promotion as well.

This tutorial is about image classification on the Zalando Clothing Store dataset using Monk Library. In this dataset, there are high-res, color, and diverse images of clothing and models. This is the best tool to use for competitions held in platforms like Kaggle, Codalab, HackerEarth, AiCrowd, etc. For other ways to install, visit Monk Library. This section is to give you a demo of this classifier before getting into further details.

Every day there is something new going on in the world of AWS Machine Learning--from launches to new use cases like posture detection to interactive trainings like the AWS Power Hour: Machine Learning on Twitch. Check back at the end of each month for the latest roundup. As models become more sophisticated, AWS customers are increasingly applying machine learning (ML) prediction to video content, whether that's in media and entertainment, autonomous driving, or more. Want more news about developments in ML? Check out the following stories: Also, if you missed it, see the Amazon Augmented AI (Amazon A2I) Tech Talk to learn how you can implement human reviews to review your ML predictions from Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon SageMaker, and other AWS AI/ ML services. See you next month for more on AWS ML! Laura Jones is a product marketing lead for AWS AI/ML where she focuses on sharing the stories of AWS's customers and educating organizations on the impact of machine learning.

We're pleased to announce enhancements to recommendation filters in Amazon Personalize, which provide you greater control on recommendations your users receive by allowing you to exclude or include items to recommend based on criteria that you define. For example, when recommending products for your e-retail store, you can exclude unavailable items from recommendations. If you're recommending videos to users, you can choose to only recommend premium content if the user is in a particular subscription tier. You typically address this by writing custom code to implement their business rules, but you can now save time and streamline your architectures by using recommendation filters in Amazon Personalize. Based on over 20 years of personalization experience, Amazon Personalize enables you to improve customer engagement by powering personalized product and content recommendations and targeted marketing promotions.