Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$. This problem seeks to approximate $A$ by a matrix whose spectrum is the same as $\Lambda$ and, by setting $\Lambda$ to be appropriate diagonal matrices, one can recover matrix approximation problems such as PCA and rank-$k$ approximation. We study the problem of designing differentially private algorithms for this optimization problem in settings where the matrix $A$ is constructed using users' private data. We give efficient and private algorithms that come with upper and lower bounds on the approximation error. Our results unify and improve upon several prior works on private matrix approximation problems. They rely on extensions of packing/covering number bounds for Grassmannians to unitary orbits which should be of independent interest.

On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the efficient candidate generation from the billion-scale item pool. Previous attempts to integrate the merits of both paradigms mainly resort to a sequential mechanism, which builds the on-device recommender on top of the cloud-based recommendation. However, such a design is inflexible when user interests dramatically change: the on-device model is stuck by the limited item cache while the cloud-based recommendation based on the large item pool do not respond without the new re-fresh feedback. To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller in the device-cloud collaboration.

Netflix is a subscription-based streaming platform that allows users to watch movies and TV shows without advertisements. One of the reasons behind the popularity of Netflix is its recommendation system. Its recommendation system recommends movies and TV shows based on the user's interest. If you are a Data Science student and want to learn how to create a Netflix recommendation system, this article is for you. This article will take you through how to build a Netflix recommendation system using Python.

Prime Day is still a week away, but Amazon is getting the jump on one of its biggest events of the year by putting a bunch of its own products on sale a little early. One of those is the fourth-gen Echo Dot. The company has slashed the price of the Alexa-powered smart speaker by 60 percent for Prime members. It's down to $20, which is $30 off the regular price. That's the best price we've seen to date.

If you're in the market for a new streaming device, chances are you want to improve your home entertainment experience. Maybe you've been relying on your phone or tablet for binge-watch sessions, or perhaps your TV's built-in operating system just isn't cutting it anymore. Streaming dongles and set-top boxes are ubiquitous at this point, but sussing out the differences between them can be challenging. Plus, they're not the only gadgets that can deliver your latest Netflix obsession to your TV screen. Let us break down all of the streaming device options you have today and give you our picks for the best you can buy.

Lior Rokach is a computer scientist. He is a professor and the former chair of the Department of Software and Information Systems Engineering (SISE) at Ben-Gurion University of the Negev (BGU). Lior was born in 1972 in Holon, Israel. He completed his B.Sc., M.Sc., and Ph.D. in 1998,1999, and 2004 respectively at Tel-Aviv University. His research interests lie in designing and analyzing Machine Learning and Data Mining algorithms and their applications in Recommender Systems, Cyber Security, and Medical Informatics.

You may be familiar with Bixby. Get ready to meet Xena, the workplace AI office assistant designed to take all the mundane tasks off your To-Do List. The idea is a part of the Xembly platform (itself billed as an "Automated Chief of Staff"), a new start-up led by some industry heavyweights like Pete Christothoulou from Marchex, Jason Flaks from Microsoft, and Peter Francis from Qualtrics. It has backing from the likes of Lightspeed Venture Partners, DocuSign founder Tom Gonser, and former Microsoft CXO Julie Larson-Green. That's why you should probably get ready for it – with that sort of backing, it'll be finding its way into your business eventually, even if it's not quite as soon as you might like.

Former aides of ex-President Donald Trump have released a new dating app for conservatives. The dating app, named The Right Stuff, is founded by former White House staffers, including John McEntee, Trump's former personal aide and ex-director of the White House presidential personnel office; and Daniel Huff, a Trump appointee in the Department of Housing and Urban Development. The dating app is designed to help conservatives "connect in authentic and meaningful ways." The Right Stuff is also created to "bring people together with shared values and similar passions," according to the website. The app is backed by Peter Thiel, a German-American tech billionaire who co-founded PayPal, Palantir Technologies, and Founders Fund.

The digitization of all manner of services has introduced recommendation systems into many aspects of our day-to-day lives. In particular, recommendation systems are now being applied to safety-critical domains such as making lifestyle recommendations to patients in healthcare [Hammer et al., 2015, Tran et al., 2021]. It is therefore increasingly important that deployed recommender systems do not output recommendations devoid of uncertainty annotations. Meaningful recommendations should come with transparent and reliable statistical assessments. To date, the majority of deployed systems have fallen far short of this desideratum [Covington et al., 2016, Liu et al., 2017, Geyik et al., 2018]. Augmenting recommendation systems with internal tracking of statistical error rates would unlock new capabilities and applications. One such capability is the ability to enforce auxiliary constraints while still guaranteeing a baseline number of high-quality items in each slate of recommendations. For example, we could diversify slates whose quality we are confident in, while leaving lower-confidence slates untouched. Furthermore, the strong guarantees provided by uncertainty quantification are a prerequisite for applying recommendation systems to safety-critical tasks such as medical diagnosis, where a misdiagnosis due to uncertain predictions can be fatal.

Albert Einstein once said: "The ideals that have lighted my way, and time after time have given me new courage to face life cheerfully, have been Kindness, Beauty and Truth." You don't often hear these words in the digital world. How do we integrate these life essentials in technologies like artificial intelligence (AI), machine learning, edge computing, internet of things (IoT) and data at scale? Technology, after all, makes things less personal, right? One company is working hard to disprove this assumption.