Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks. We present an enhanced DPP model that is specialized for the task of basket completion, the multi-task DPP. We view the basket completion problem as a multi-class classification problem, and leverage ideas from tensor factorization and multi-class classification to design the multi-task DPP model. We evaluate our model on several real-world datasets, and find that the multi-task DPP provides significantly better predictive quality than a number of state-of-the-art models.

It's no secret that platforms like Instagram and Snapchat have transformed the way shoppers interact with brands. From influencers to sponsored posts, the way we shop has drastically changed in the last ten years. In response to this changing environment, many entrepreneurs have merged shopping with social platforms to create seamless experiences for both consumers and retailers. Chelfie, a Virginia-based social shopping app, is one of these solutions. Their platform allows users to receive live feedback on what to wear by sharing images privately or with a broader community of influencers.

Amazon founder and CEO Jeff Bezos laughs as he talks to the media while touring the new Amazon Spheres during the grand opening at Amazon's Seattle headquarters in Seattle, Washington, U.S., January 29, 2018. Amazon has been selling a facial-recognition system to police, sparking fears that the technology will one day power mass surveillance. On Tuesday, the American Civil Liberties Union and 35 other advocacy group sent a letter to the company's CEO Jeff Bezos, demanding that he stop providing the technology to law enforcement. The technology, called Amazon Rekognition, can identify people's faces in digital images and video. Police in Oregon and Florida have been using the system to help them solve crimes, but the ACLU argues that it's ripe for abuse.

The British glove manufacturer Cornelia James is often in the public eye. Since 1979, it has provided gloves by royal appointment to the Queen of England, and more recently it has dressed megastars including Rihanna, Taylor Swift and Madonna. All those high-profile customers are great for publicity, but nobody drives sales quite like Kate Middleton, said Genevieve James, the company's creative director. "We get far more of a spike in sales when someone like Kate wears them. But when it's somebody like the Duchess of Cambridge, the public, particularly in the US, can relate to that," James told IBM. James first confirmed that fact in 2012 when, at a ceremony for Remembrance Sunday, Middleton wore a pair of Cornelia James' merino wool gloves.

Recently, I met a few friends in preparation for a machine learning panel on e-commerce that will be happening in Seattle on May 31st. Our goal is to answer retailers real business problems with data science, and data analytics solutions. We started to discuss ways we could see data being used to improve different brick and mortar stores as well as e-commerce sites. One of our discussions led us to talking about Men's Wearhouse. We had originally started by discussing the problems with buying clothes online.

Engineers at Ocado Technology developed a robotic arm capable of lifting up thousands of the retailer's catalog items. For more videos, subscribe to Mashable News: http://on.mash.to/SubscribeNews Give us a follow: Facebook: https://www.facebook.com/mashable/

While AI has worked its way into countless sectors bringing changes to our everyday lives, the online retail industry, in particular, has been significantly transformed by the adoption of these new capabilities. The acceptance and growth of AI in e-commerce this year confirms what I believe: that the shopping experience of the future -- both online and in-store -- will be dominated by AI tech for years to come.

The lights are going out at malls across the United States with more than 20 major retail bankruptcies in 2017. As of today, store closures have skyrocketed to 7,000 doors throughout the nation, affecting such iconic brands as Toys R Us, Walgreens, Gap, Sam's Club, The Children's Place, Hallmark, Stride Ride, Aeropostale, Wet Seal, The Limited and Walmart. At the same time, investment in retail technology has never been higher, especially robots.A month after Walmart laid off close to 10,000 workers with the shuttering of Sam's Club, it announced a new partnership with Pittsburgh-based robot manufacturer, Bossa Nova. The mechatronics innovator will begin rolling out inventory auditing scanning bots to 50 Walmart locations. The machines will automate the tasks previously held by inventory associates by autonomously navigating around the store to check the shelf display, inventory position, and pricing of the big box's 200,000 items.

Ocado's fulfilment center in Andover has hundreds of robots working above a grid of groceries. In the race to stay alive in grocery retailing, Whole Foods sold to Amazon, Walmart offered grocery delivery and now Kroger has announced it will build three huge, automated warehouses filled with swarming robots to process online orders. The only problem: By the time Kroger gets those warehouses, which could rise to 20 in the coming years, it may still struggle to match Amazon in doing speedy delivery. The reason comes down to geography and the nitty-gritty details of Kroger's logistical overhaul. Kroger has picked warehouse builder Ocado, well known in the U.K. for being one of the first companies in the world to sell fresh food online when it first launched in 2000.

Successful retailers are data-driven retailers. The task of predicting consumer behavior, with an eye toward understanding individual customers, involves working with dozens of variables and data that is often incomplete, ambiguous, and organized for transaction processing and not analytics. The promise of data science, artificial intelligence (AI) and machine learning is for these techniques to help spot trends and patterns with sufficient speed and accuracy for retailers to be able to adjust floor-sets, advertising, discounting, and product mix as quickly as possible. But just as data scientists are becoming more commonplace, antagonism is growing between expectations about how quickly algorithms and advanced mathematical techniques can transform retailers, and the reality that data science is, well, science. Just as with the physical sciences, there are many dead ends and failures in data science.