We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Recommendation for e-commerce with a mix of durable and nondurable goods has characteristics that distinguish it from the well-studied media recommendation problem. The demand for items is a combined effect of form utility and time utility, i.e., a product must both be intrinsically appealing to a consumer and the time must be right for purchase. In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession. Moreover, purchase data, in contrast to rating data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware recommendation problem that we pose via joint low-rank tensor completion and product category inter-purchase duration vector estimation. We further relax this problem and propose a highly scalable alternating minimization approach with which we can solve problems with millions of users and millions of items in a single thread. We also show superior prediction accuracies on multiple real-world datasets.

WHEN the first printed books with illustrations started to appear in the 1470s in the German city of Augsburg, wood engravers rose up in protest. Worried about their jobs, they literally stopped the presses. In fact, their skills turned out to be in higher demand than before: somebody had to illustrate the growing number of books. Fears about the impact of technology on jobs have resurfaced periodically ever since. The latest bout of anxiety concerns the arrival of artificial intelligence (AI).

Xiaomi revealed through its Mi Community that it will offer a new voice assistant speaker for $30 in January. Sold by Xiaomi subsidiary Yeelight, the big deal with this device is that it will sport two virtual assistants: One based on Xiaomi artificial intelligence technology to be used within China, and Amazon Alexa for customers located in the Western market. This dual-A.I. support enables Yeelight to sell the voice assistant speaker on a global scale. Although Xiaomi and Yeelight didn't elaborate on the stated China-based A.I. technology, speculation points to possible Microsoft Cortana integration. Microsoft and Xiaomi made a deal in May 2016 that would bring Word, Excel, PowerPoint, Outlook, and Skype to Xiaomi phones as pre-installed apps.

This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, -0.24% GOOG, -0.17% search engine for years. Behind the scenes, however, the response to this type of query now leverages an enormous amount of machine-learning technology that Google has spent years and billions of dollars developing, in hopes of being a leader in artificial intelligence. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year.

A common theme among some of the most notable advances and new devices was the integration of artificial intelligence in smart and innovative ways. Despite a handful of flubs, AI-powered technologies still helped make the world a little smarter, kinder, and more innovative this year. Here are some of the moments when AI really shone in 2017. Earlier this month, NASA announced it used machine learning to discover two new planets. Researchers used old data from the Kepler space telescope to locate the two new additions to our galaxy. This wasn't the first time researchers applied AI to sift through the massive amount of data NASA's telescopes collect, but it is a promising example of how neural networks can find even some of the weakest signs of distant worlds. Thanks to AI, we have now discovered a planetary system that ties our solar system in the number of planets it has, which brings us one step closer to discovering more of the mysteries the giant void around us contains.

No matter how many Amazon Echo commercials you see, it takes a little time to adjust to Alexa. Putting a virtual assistant in your home signals a change in lifestyle, sort of like adopting a puppy. There will be a lot of trial-and-error, but once you find your rhythm, you'll forget what life was like without her. The Amazon Echo listens for the wake word, "Alexa." But, frankly, I was shocked by how many conversations were recorded by my Echo that did not include the wake word. Click here to learn how to listen to everything Amazon Echo has ever heard.

For any media company that's producing and distributing large volumes of content, highly specific metadata is the foundation that other publisher tools will be built upon. Metadata provides a way of finding, identifying and classifying videos thus making them highly searchable and shareable. Using Deep Learning, Vilynx exposes the multi-level intelligence of video assets to enable publishers more efficient content management. Most recommendation systems rely on the relationships between the content in videos to provide appealing suggestions about which other videos you might also want to see. To solve this, some publishers have resorted to semantic analysis of content as well as basic collaborative filters to power content recommendations.

They may be older and wiser, but when it comes to technology sometimes it seems the more senior generation still has a bit to learn. This hilarious but endearing video of an 85-year-old woman trying to use a new Google Home speaker is proof of that. YouTuber Ben Actis uploaded the clip on Wednesday, and since then almost 100,000 people have tuned in to watch his Italian grandmother attempting to work the gadget. After being told she must say'Hey Google' the woman can be heard calling it'goo goo' as she tries to request a song and ask it for a weather forecast. Her strong Italian accent gets in the way slightly but she seems to have a difficult time understanding how to activate the gadget, as she bashes it with her fingers in an attempt to turn it on.

Thanks to ever more rapidly advancing modern technology, we hardly have to move from the comfort of our beds to complete anything from shopping to scheduling appointments. But sometimes it's not all it's cracked up to be, as a series of hilarious viral tweets has proved. From receiving unwanted sex toys through the post, to witnessing rather spooky'flirtations' between an Alexa and a Google Home, perplexed gadget lovers have been sharing their virtual assistants' biggest fails on social media... Modern technology is advanced as ever, but sometimes it's not all it's cracked up to be, as a series of hilarious viral tweets have proved Perhaps the old school way really is the best? A peckish child was left disappointed after their McDonald's order turned out to be a book Things that go bump in the night! Alexa scared her owners when she let out a'ghostly wail' out of nowhere Alexa decided she'd rather turn on a lullaby than the lights... surely making for a rather dark musical experience A tweeter was left embarrassed after Alexa pointed out he didn't have a Porsche, when prompted to turn on the porch lights When add pampers turns to add cancer: One Tweeter was left perplexed by Alexa's apparent hearing impediment Alexa's attempt at initiative was quickly shut down by one owner'Do something': Alexa gave one owner a rather unhelpful #alexafail reminder The comments below have not been moderated.