Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper, we propose a Neural Attention mechanism for Social collaborative filtering, namely NAS. We design a neural architecture, to carefully compute the non-linearity in friends' preferences by taking into account the social latent effects of friends on user behavior. In addition, we introduce a social behavioral attention mechanism to adaptively weigh the influence of friends on user preferences and consequently generate accurate recommendations. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed NAS model over other state-of-the-art methods. Furthermore, we study the effect of the proposed social behavioral attention mechanism and show that it is a key factor to our model's performance.

If you've taken the leap and added this cute little button of a machine to your smart home arsenal, congratulations! The Google Home Mini is about to revolutionize your life in ways you probably haven't realized it was even capable of yet. While you won't ever get the same sound quality out of the Google Home Mini as you could expect from it's OG sibling (the Google Home), it is comparable in pretty much every other way. Fun fact: If you've got multiple Google devices within earshot (your phone, the Google Home, a few Google Home Minis…) and they all hear you call out an "OK, Google," or "Hey, Google," at once, they'll sync up to ensure only one replies. And that's just the beginning of what this mini smart speaker is capable of.

Artificial intelligence, or AI as it's called, has been a buzzword for nearly a decade already, yet sometimes it still feels as though we're just in the early stages of discovering what predictive analytics and machine learning can do for enterprises. Nowhere is this truer than in marketing and sales functions. According to Forrester, as of 2017 marketing and sales accounted for more than 50 percent of all AI investments. But when you look at investors who have already sunk serious money into AI projects, only 45 percent have seen any results at all. And among those who are seeing results, 25 percent agree that they've become more effective in their business processes.

The Internet of Things is taking on a larger role in the home. From voice assistants like Google Home and Amazon Echo to smart appliances, tech companies are on a mission to make everyday life easier through IoT devices. With technology, of course, there's always something new around the corner, and it's expected that artificial intelligence (AI) and machine learning (ML) will continue to expand the capabilities of IoT devices. To get an insider's perspective on what may be coming, we asked 11 Forbes Technology Council members to share what they think will be the next big thing in at-home IoT tech. IoT systems learning an individual's patterns, habits, preferences and autonomously operating are essential with the growing number of IoT devices at home.

Interaction function (IFC), which captures interactions among items and users, is of great importance in collaborative filtering (CF). The inner product is the most popular IFC due to its success in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Many other operations (such as plus and concatenation) have also been proposed, and can possibly offer better performance than the inner product. In this paper, motivated by the success of automated machine learning, we propose to search for proper interaction functions (SIF) for CF tasks. We first design an expressive search space for SIF by reviewing and generalizing existing CF approaches. We then propose to represent the search space as a structured multi-layer perceptron, and design a stochastic gradient descent algorithm which can simultaneously update both architectures and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, and also obtain much better prediction performance than state-of-the-art CF approaches.

Systems that make sequential decisions in the presence of partial feedback on actions often need to strike a balance between maximizing immediate payoffs based on available information, and acquiring new information that may be essential for maximizing future payoffs. This trade-off is captured by the multi-armed bandit (MAB) framework that has been studied and applied for designing sequential experiments when at each time epoch a single observation is collected on the action that was selected at that epoch. However, in many practical settings additional information may become available between decision epochs. We introduce a generalized MAB formulation in which auxiliary information on each arm may appear arbitrarily over time. By obtaining matching lower and upper bounds, we characterize the minimax complexity of this family of MAB problems as a function of the information arrival process, and study how salient characteristics of this process impact policy design and achievable performance. We establish the robustness of a Thompson sampling policy in the presence of additional information, but observe that other policies that are of practical importance do not exhibit such robustness. We therefore introduce a broad adaptive exploration approach for designing policies that, without any prior knowledge on the information arrival process, attain the best performance (in terms of regret rate) that is achievable when the information arrival process is a priori known. Our approach is based on adjusting MAB policies designed to perform well in the absence of auxiliary information by using dynamically customized virtual time indexes to endogenously control the exploration rate of the policy. We demonstrate our approach through appropriately adjusting known MAB policies and establishing improved performance bounds for these policies in the presence of auxiliary information.

Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heavily rely on human experts to define prediction tasks, and generate labels by aggregating raw data. In this paper, we tackle the challenge of defining useful prediction problems on event-driven time-series data. We introduce MLFriend to address this challenge. MLFriend first generates all possible prediction tasks under a predefined space, then interacts with a data scientist to learn the context of the data and recommend good prediction tasks from all the tasks in the space. We evaluate our system on three different datasets and generate a total of 2885 prediction tasks and solve them. Out of these 722 were deemed useful by expert data scientists. We also show that an automatic prediction task discovery system is able to identify top 10 tasks that a user may like within a batch of 100 tasks.

Product suggestions are an ingrained part of the ecommerce experience. With the up- and cross-selling opportunities that a good system can provide, a thoughtful ecommerce experience is invaluable, as Amazon's paid search and display advertising strategy has shown. For consumers, suggested products should bring real value. Rather than being haunted for weeks by a product they searched for once, consumers should experience helpful product suggestions which complement their purchases. Often, this is the case.

If you've ever felt totally exhausted like you're at the end of your rope and done with everything, odds are you've said, I'm burned out. Whether it's from work, your personal life or both, burnout is increasingly common, and it's affecting how we date. I swiped through an endless sea of faces and went on six first dates in 10 days. It was exhausting, so I deleted the app. A couple weeks later, I re-downloaded it, swiped, and the cycle repeated.

TTEC Holdings, Inc., a leading digital global customer experience technology and services company focused on the design, implementation and delivery of transformative customer experience for many of the world's most iconic and disruptive brands, will be showcasing Associate Assist and other innovative technology solutions for AI-enhanced training, omnichannel interactions and journey orchestration during Customer Contact Week, June 24-27, in Las Vegas. TTEC creates employee experiences that increase engagement and designs, builds and operates customer experiences that deliver results. TTEC uses Intelligent Virtual Assistants (IVAs) to empower employees and deliver seamless service experiences that enable hyper personalization, increase response time and improve accuracy. Associate Assist augments associates by monitoring conversations between associates and customers and scanning through data to deliver the suggested next best action or response to the associate, in real-time. In addition, the solution establishes a closed loop, AI-enhanced, self-training knowledge base that is used not only to train new associates but also improve associate accuracy, efficiency and consistency.