Artificial intelligence (AI) has been a game-changer for the retail and E-commerce industries. According to Statista, retail sales are projected to amount to around $30 trillion by 2023. According to Nasdaq, 95% of purchases will be facilitated by E-commerce by 2040. No doubt, AI will be shaping retail digitization. AI has pretty much to offer the retail industry.

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard" unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.

It's a very nice set, although we missed the deep black performance we've seen in some competitors outfitted with mini-LED backlights. The U8G-series is available in both 55-inch ($950) and 65-inch ($1,300, reviewed here) sizes. It uses a 120Hz, 10-bit, 4K UHD (3840 x 2160) panel featuring quantum dots for extremely accurate color. The TV is a bit on the heavy side, weighing close to 53.4 pounds on the wall (with VESA 400 mm x 400 mm mount), and 56 pounds including the stand. The bezel is thin, and there's a classy look to the whole deal.

As one of the leading trends in technology, Artificial Intelligence (AI) continues to gain popularity among marketers and sales professionals, and has become an essential tool for brands wanting to deliver an exceptional and hyper-customized customer experience. The availability of AI-enhanced Customer Relationship Management (CRM) and Customer Data Platform (CDP) software has significantly reduced costs previously associated with the technology. A report on the future of work indicated that 39% of IT leaders are currently using AI or machine learning, 33% said they expect to use Artificial Intelligence in the next three years and 19% are confident to use it within five years. The combination of Artificial Intelligence and machine learning to collect and analyze social, historical and behavioral data enables brands to gain a much more accurate understanding of their customers. Unlike traditional data analytics software, AI continuously learns and improves from the data it analyzes and is able to anticipate customer behavior.

If you have ever owned an Apple TV streaming device, then you know how easy it is to lose its remote. Its thin frame becomes its downfall when confronted with the comfy spaces between couch cushions. Naturally, when Apple recently introduced AirTags, a tiny disc capable of tracking your stuff through the iPhone's Find My app, some people joked we could finally keep track of that easy-to-lose Apple TV remote. During an interview with the website MobileSyrup, Tim Twerdahl, Apple's vice president of product marketing for home and audio, said upgrades to the size of the Siri-enabled Apple TV remote were enough to make it less likely to lose without incorporating technology similar to AirTags. "With the changes we've made to the Siri Remote – including making it a bit thicker so it won't fall in your couch cushions as much – that need to have all these other network devices find it seems a little bit lower," said Twerdahl.

In this piece, we cover how AI can help personalize the customer experience, leading to higher satisfaction rates and greater revenue growth. Customers are used to getting a personalized experience from each company they interact with. You can personalize the experience of your customers by building effective recommender systems. These are the systems that personalize product placement and search results for each consumer. When you recommend products or content that customers are more likely to purchase, this gives the customer a better sales experience while driving more revenue for businesses through cross-selling and up-selling.

The banking industry is in the middle of a significant transformation. Fintech startups and other non-traditional companies are gaining market share at the cost of traditional banks, and customer attrition is at an all time high. According to Boston Consulting Group, corporate banks lose between 10–15% of gross revenues due to attrition annually -- there was a 15% attrition rate amongst retail financial institutions per year (Source: Boston Consulting Group). So why is the attrition rate so high with traditional banks? The main reason is that customers are dissatisfied with their experience at their bank.

Between video calls and workouts at home, it felt like I was constantly putting some sort of implement in my ear. Wireless earbuds have become essential--as has noise-canceling technology to drown out the sounds of housemates. If you're looking for a new pair, and are leery of dropping $249 on Apple's shiny Pro'pods, consider Amazon's recent update to its Bluetooth buds. The second-generation Echo Buds have active noise cancellation and built-in, hands-free Alexa. The price--$120, or $140 with a wireless charging case--is why these headphones are worth your attention.

There is no doubt that today we have embarked on the path of technologies that will perform many tasks the accomplishment of which, for now, requires the human brain. Companies like Google, Facebook, Amazon, Apple, Microsoft, and Adobe are investing heavily in technologies that automate and anticipate business and domestic activities. At the head of the list of the most important users of artificial intelligence, we find unsurprisingly social networks, search engines, and SaaS. Far beyond targeted advertising, technology has enabled structures of all sizes to expand their customer base on an international scale. Changes in purchasing behavior and the multitude of contact channels have exploded the volume of data available to brands.

It's no secret that artificial intelligence (AI) is already changing the way we deal with money, and it has the potential to revolutionize banking as we know it. AI is already a common tool used for some up-and-coming fintechs, whether that's a robo-advisor or an automated-savings tool. Traditional banks are also recognizing its potential to offer a more personalized service for customers amid branch closures -- HSBC and Wells Fargo offer an AI experience that mimics a real-life assistant. However, while there's certainly an interest in and understanding of the technology's potential, the process so far has been slow, complex and difficult. Banks wishing to adopt AI to make a real change are faced with regulation, risk management and limitations within their own organizations.