Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorized version of IMC is more favored due to its efficiency of optimization and implementation. Regarding the factorized version, traditional IMC method can be interpreted as learning an individual representation for each feature, which is independent from each other. Moreover, representations for the same features are shared across all users/items. However, the independent characteristic for features and shared characteristic for the same features across all users/items may limit the expressiveness of the model. The limitation also exists in variants of IMC, such as deep learning based IMC models. To break the limitation, we generalize recent advances of self-attention mechanism to IMC and propose a context-aware model called collaborative self-attention (CSA), which can jointly learn context-aware representations for features and perform inductive matrix completion process. Extensive experiments on three large-scale datasets from real RS applications demonstrate effectiveness of CSA.

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student's prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student's language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.

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.