Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and the patterns in the rating matrix itself is rarely touched. In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices {\em only} without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models.

Some people use the terms "artificial intelligence" and "machine learning" interchangeably, but they're not the same thing. To put it simply, artificial intelligence is a broad computer science concept that encompasses the idea of machines displaying cognitive abilities. These abilities range from visual perception and speech recognition to decision-making. Think anything from Amazon's Alexa to Hanson's Robotics' Sophia. Machine learning, on the other hand, is only one of the applications (or subfields) of AI.

I'm the Azure content lead at Cloud Academy and I have over 10 years of experience with cloud technologies. If you have any questions, feel free to connect with me on LinkedIn and send me a message or send an email to support@cloudacademy.com. This course is intended for people who are interested in artificial intelligence services on Azure especially recommendation engines. To get the most from this course, it would be helpful to have some experience using Azure. Ideally, you should also have some experience using APIs, although that's not strictly necessary.

In this video, you'll learn about Microsoft's Product Recommendation Solution. Watch the full course https://cloudacademy.com/course/build... to learn how to use artificial intelligence to add product recommendations to your website using Azure resources. You'll learn the essentials of building, deploying and testing a recommendation engine on Microsoft Azure. You will also build skills to fine-tune a recommendation model and evaluate its effectiveness. Some Azure and API experience is recommended.

In this episode, we talk to Nick Saretzky, Senior Director of Project Management at Tinder, about how dating apps started out with data, most recently with Tinder data. We discuss the benefits of driving change through data insights, and what user data Tinder has at its disposal. We also talk about the impact of dating apps on how people interact, and on the changing approach to modern relationships. Listen to this episode on Spotify, iTunes, and Stitcher. You can also catch up on the previous episode of the Hub & Spoken podcast, in which Jason spoke to Kerry Dawes, Director of Digital Customer Experience at The Rank Group, on the impact of data on the digital customer experience in gambling.

As we live longer and technology continues its rapid arc of development, we can imagine a future where machines will augment our human abilities and help us make better life choices, from health to wealth. Instead of conducting a question and answer with a device on the countertop, we will be able to converse naturally with our virtual assistant that is fully embedded in our physical environment. Through our dialogue and digital breadcrumbs, it will understand our life goals and aspirations, our obligations and limitations. It will seamlessly and automatically help us budget and save for different life events, so we can spend more time enjoying life's moments. While we can imagine this future, the technology itself is not without challenges -- at least for now.

"AI is being fed directly into the bloodstream of society, and in many cases without sufficient checks and balances," says Kate Crawford, a professor and cofounder of New York University's AI Now, the world's first academic research institute dedicated to the social impact of artificial intelligence. Last year, Crawford partnered with data-viz guru Vladan Joler to create "Anatomy of an AI System," a map and research paper demonstrating the real-world consequences of developing and manufacturing the Amazon Echo. The paper highlights the radical differences in income distribution between Amazon executives and the workers who enable its vast infrastructure, as well as its devastating environmental impacts. The project has been exhibited at museums around the world, and Crawford has presented it to leaders in France, Germany, Spain, and Argentina.

In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans. But true artificial intelligence, as McCarthy conceived it, continues to elude us.

Mobile maps route us through traffic, algorithms can now pilot automobiles, virtual assistants help us smoothly toggle between work and life, and smart code is adept at surfacing our next our new favorite song. But AI could prove dangerous, too. Tesla CEO Elon Musk once warned that biased, unmonitored and unregulated AI could be the "greatest risk we face as a civilization." Instead, AI experts are concerned that automated systems are likely to absorb bias from human programmers. And when bias is coded into the algorithms that power AI it will be nearly impossible to remove.

Google has unveiled updates for its artificially intelligent voice assistant and new privacy tools to give people more control over how they're being tracked on the go or at home. The company also unveiled a new Pixel phone and smart home display. Google just made ordering pizza, pad thai and fried chicken from your favorite restaurants even easier. The search giant announced on Thursday that it updated apps like Google Maps, Google Search and the Google Assistant to make ordering food online more convenient, so you don't have to download as many third-party apps. "When I was pregnant with my son last year, my cravings were completely overpowering," said Google's senior product manager of food ordering, Anantica Singh, in a blog post.