Online kernel learning (OKL) is a flexible framework to approach prediction problems, since the large approximation space provided by reproducing kernel Hilbert spaces can contain an accurate function for the problem. Nonetheless, optimizing over this space is computationally expensive. Not only first order methods accumulate $\O(\sqrt{T})$ more loss than the optimal function, but the curse of kernelization results in a $\O(t)$ per step complexity. Second-order methods get closer to the optimum much faster, suffering only $\O(\log(T))$ regret, but second-order updates are even more expensive, with a $\O(t^2)$ per-step cost. Existing approximate OKL methods try to reduce this complexity either by limiting the Support Vectors (SV) introduced in the predictor, or by avoiding the kernelization process altogether using embedding. Nonetheless, as long as the size of the approximation space or the number of SV does not grow over time, an adversary can always exploit the approximation process. In this paper, we propose PROS-N-KONS, a method that combines Nystrom sketching to project the input point in a small, accurate embedded space, and performs efficient second-order updates in this space. The embedded space is continuously updated to guarantee that the embedding remains accurate, and we show that the per-step cost only grows with the effective dimension of the problem and not with $T$. Moreover, the second-order updated allows us to achieve the logarithmic regret. We empirically compare our algorithm on recent large-scales benchmarks and show it performs favorably.

Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction \alpha of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c / (1 - \alpha) for each item, but it is not the optimal solution on maximizing the profit of the intermediary. While previous studies (by Mirrokni and Gomes, and by Niazadeh et al) focused on revenue-sharing schemes in static double auctions, in this paper, we take advantage of the repeated nature of the auctions. In particular, we introduce dynamic revenue sharing schemes where we balance the two constraints over different auctions to achieve higher profit and seller revenue. This is directly motivated by the practice of advertising exchanges where the fixed-percentage revenue-share should be met across all auctions and not in each auction. In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation. Finally, we empirically evaluate our revenue sharing scheme on real data.

David has been a leader in bringing technology and insights to the philanthropic community for a generation. He created the first asset-based wealth screening service as well as the first software to manage screening data. In 1997, he founded Prospect Information Network (P!N), which became the largest wealth screening company before being purchased in 2004. P!N received the InfoCommerce Model of Excellence Award and introduced the first Software-as-a-Service application to support fundraising analytics. David is CEO and co-founder of NewSci, LLC.

Want to know how it feels to walk through a labyrinth? But at the nation's largest shopping center, the Mall of America in Minnesota, a chatbot can intervene before you find yourself wandering the premises like a lost child. By early next year, three humanoid robots will also be on hand to give shoppers a nudge in the right direction. On Black Friday, the mall gave customers a preview of the humanoid, Pepper, and on December 4th, it introduced a new chatbot designed by Satisfi Labs. The retail chatbot can field complex questions and even guide patrons to specific businesses or attractions in the mall based on their location.

Strategising marketing, advertising and sales efforts is no longer a Herculean task for brands and retailers, with many elevating consumer experiences with the help of artificial intelligence, machine learning and big data. Since customers in today's digital world demand hyper-personalised content and tailored experiences from their favourite brands, several e-tailers are perfecting their moves. "Advanced AI capabilities, that we have only known through science-fiction movies, such as chatbots and virtual shopping assistants, are quite real now, and many online retailers are already using them to serve customers in a better, more interactive manner," Sudeshna Datta, co-founder and Executive Vice-President, AbsolutData, an analytics firm, told BusinessLine. While Alibaba is using AI to create personalised shopping experiences for its consumers, Amazon has introduced and perfected some of the key use-cases for AI, machine learning, and analytics in e-commerce marketing, in addition to developing innovative communication strategies. Flipkart, too, has stepped on the gas, and is keen to utilise its e-commerce data on consumer behaviour that is has collated over the past 10 years.

By Sameet Gupte Customer experience (CX) has become the quintessential scale to choose between brands, and poor experience is a way of falling short in the buyer's journey to seamless interaction. However, over time businesses have begun to adopt and understand that a key component to building their CX strategies is around the customer. With rising expectations, the climb to the top is getting steeper in this rapidly evolving space. Each year there are predictions that CX will make the purchase decision easier and further improve the customer-business relationship. It has been rightly doing so with the increasing number of communication channels, CX metrics, and tools to support the point of contact.

Amazon will hold its second annual Digital Day sale on Friday Dec. 29, in which it will offer deals on games, apps, movies and music. The Digital Day sale will follow Amazon's successful Black Friday and Cyber Monday, as well as the company's record-breaking holiday season. Digital Day will offer more than 5,000 deals across the shopping platform. This year around, the company is holding 40 percent more deals than in 2016, Amazon said. The online retailer will offer up to 80 percent off digital content, which includes apps, games, TV shows, movies, digital comics and more.

Summary: In this review of Mary Meeker's annual Internet Trends report for 2016 we'll look for the advanced analytics that makes these trends possible. It's that time of year again when Mary Meeker, the great seer of the internet once again releases her annual Internet Trends 2016 report. If by chance you don't know who Ms. Meeker is she is a partner in the VC firm Kliener Perkins and is acclaimed by Forbes to be the 77th most powerful woman in the world. She started issuing these reports in 1995 at Morgan Stanley and was and is still widely regarded as a guru of the internet. Say what you will, and she is a bit of controversial character, she gathers and analyzes voluminous amounts of data and has a well deserved track record for spotting big picture trends before they are widely recognized.

Amazon saw a big win this holiday season. The company took over with its Echo devices after Apple's HomePod was delayed. Amazon announced on Tuesday the Echo Dot was the top-selling device across all categories on the shopping platform this holiday season. The company said it sold tens of millions of Alexa-enabled gadgets worldwide, selling millions of Echo Dot devices alone. The Echo Dot was also the top-selling gadget across Amazon Books' 13 stores, the company said.

The CEO and founder of Amazon.com is listed as the world's richest person with a net worth of $99.6 billion. Thanks to a surge in Amazon's share price, the 53-year-old added $34.2 billion to his wealth to round out a standout year for the tech and retail giant. The Seattle-based company has grown from its online retail roots to cloud computing, streaming video, artificial intelligence and more. Amazon's shares have recently been boosted by its acquisition of grocery chain Whole Foods. The firm has also expanded its line-up of devices tapping into its digital assistant Alexa.