Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce challenges to the algorithm stability of matrix approximation, i.e., small changes in the training data may significantly change the models. As a result, existing matrix approximation solutions yield low generalization performance, exhibiting high error variance on the training data, and minimizing the training error may not guarantee error reduction on the test data. This paper investigates the algorithm stability problem of matrix approximation methods and how to achieve stable collaborative filtering via stable matrix approximation. We present a new algorithm design framework, which (1) introduces new optimization objectives to guide stable matrix approximation algorithm design, and (2) solves the optimization problem to obtain stable approximation solutions with good generalization performance. Experimental results on real-world datasets demonstrate that the proposed method can achieve better accuracy compared with state-of-the-art matrix approximation methods and ensemble methods in both rating prediction and top-N recommendation tasks.

Black Friday is just around the corner, but you don't have to wait until then to grab great deals on stocking stuffers. Today, the 2nd-generation Echo Dot is $30, down from a list price of $40, or you can save $60 when you buy four, bringing them down to just $25 each. And if you're looking for a child-friendly version, the Echo Dot Kids Edition is on sale for $60, knocking $10 off the usual price. Alternatively, buying two brings them down to $55 each. We've called the Echo Dot "the Echo most people should buy," and that still holds true--especially at this price.

Abstract--Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual elementcontext relations among them. Unlike many other domains, this approach has not achieved a desired performance in collaborative filtering problems, probably due to unavailability of appropriate textual data. In this paper we propose a new recommendation framework, called GEMRank that can be applied when the useritem matrix is the sole available souce of information. It uses the concept of profile co-occurrence for defining relations among entities and applies a factorization method for embedding the users and items. GEMRank then feeds the extracted representations to a neural network model to predict user-item like/dislike relations which the final recommendations are made based on. We evaluated GEMRank in an extensive set of experiments against state of the art recommendation methods. The results show that GEMRank significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density. Recommendation Systems help users to find relevant items based on their preferences. Many prominent recommendation systems are using Collaborative Filtering (CF) for making recommendations ( [1]).

Robin E. was folding laundry when she heard her son talking to Alexa downstairs in a soft, hopeful voice. The 5-year-old was asking, "Alexa, will you be my friend?" Robin held her breath, waiting tensely for Alexa's response. Finally, she heard the assistant say, brightly, "I'm happy to be your friend." Robin and her husband have an Echo Spot in their bedroom and an Echo Show on their kitchen counter.

Patients and their families often want continuous monitoring and care. Traditional health insurance providers are partnering with telehealth companies, to address those concerns. Anthem is working with American Well, Cigna is working with MDLive, Bupa is working with Babylon Health and Aflac is working with MeMD to deliver benefits of telehealth to it's existing customers. Health insurance providers such as Oscar Health is redefining health-insurance by building the whole customer experience around its own telehealth services. As telehealth continues to replace traditional health care, it is going to inherit some of its challenges. These include increased cost of care due to multiple vendors, complex care pathways, and government policies. However, the question that remains to be answered is will this advanced technology that we call telehealth, be able to redefine the quality, equity and affordability of healthcare throughout the world.

Your message has been sent. There was an error emailing this page. Every room in your house has them: power outlets, and they're about as dumb as technology can be. To create a true smart home, you need to upgrade the way your wiring works, so that it can be controlled, managed, and monitored from afar. You could hire an electrician to rip out your existing outlets and replace them with smart in-wall models, but there's an easier, less-expensive way: Stick a smart plug in those outlets.

Palm helped popularize the idea of a modern smartphone with its Palm Treo, but after failing to keep up with the iPhone and Android the company has been reborn. Its new product: A 3.3-inch companion device to work alongside those phones, not replace it. And yet Palm's past found me wanting to embrace the newly reborn Palm, an adorable and, yes, palm-sized $349 smartphone stand-in on sale Friday as a Verizon Wireless exclusive. I have fond memories for the original PalmPilot, the first truly successful personal digital assistant. I was even a fan of the Palm Pre, a phone with software that was, in hindsight, ahead of its time but couldn't escape the iPhone's shadow.

Anki's new robo-assistant is no Alexa or Google Home – and that's exactly what gives Vector its charm. The firm released its adorable bot this month after a record-breaking Kickstarter campaign, which raked in over $1,800,000 from thousands of backers. Unlike other home AI on the market, which make efforts to mask their robotic personalities in sleek packaging, Vector fully embraces its sci-fi inspiration to give you an emotive, free-roaming helper that goes out of its way to stand out. Anki has released its adorable bot this month after a record-breaking Kickstarter campaign, which raked in over $1,800,000 from thousands of backers. Vector is capable of taking on many of the basic tasks you might ask of Siri; the $250 robot can tell you the weather, set timers, and look up the answers to your questions, be it the date of the upcoming full moon or the amount of calories in a given snack.

Google Assistant won't be arriving on Sonos this year, the company has admitted, pushing the release date into 2019. Users had hoped that Google's voice assistant would arrive alongside Alexa at some time before the end of the year. But Sonos said integrating it had taken more time than planned and so it won't arrive until 2019. It didn't say when the new features would arrive. But it noted that it was making "good progress" and it is looking to lock down a date in the next year, with another announcement coming in "early 2019".

Ever since Sonos speakers started working with Amazon's Alexa, the company has promised that it would eventually support Google Assistant as well. Well, after announcing that the Google Assistant would arrive in 2018, the company is now delaying that launch until next year. Sonos says in a press release that it needs "a bit more time to get the experience right" and is looking to get a concrete date set for 2019. It's disappointing but not terribly surprising, as Sonos has been extremely tight-lipped on the Google Assistant for months now. There is at least some movement, though: Sonos plans to start a private beta very soon; users can sign up to be considered here.