Item recommendation task predicts a personalized ranking over a set of items for each individual user. One paradigm is the rating-based methods that concentrate on explicit feedbacks and hence face the difficulties in collecting them. Meanwhile, the ranking-based methods are presented with rated items and then rank the rated above the unrated. This paradigm takes advantage of widely available implicit feedback. It, however, usually ignores a kind of important information: item reviews. Item reviews not only justify the preferences of users, but also help alleviate the cold-start problem that fails the collaborative filtering. In this paper, we propose two novel and simple models to integrate item reviews into Bayesian personalized ranking. In each model, we make use of text features extracted from item reviews using word embeddings. On top of text features we uncover the review dimensions that explain the variation in users' feedback and these review factors represent a prior preference of users. Experiments on six real-world data sets show the benefits of leveraging item reviews on ranking prediction. We also conduct analyses to understand the proposed models.

Google Home ($129 at the Google Play Store) is one of our favorite smart speakers, but its dependence on power from a wall outlet limits its mobility. Ninety7's Loft aims to solve that problem: It's a battery that attaches to your existing Google Home to provide on-the-go power, so you can easily take it with you from room to room, or into the garage or yard--or even on the road if you want to use your Google Home as just a Bluetooth speaker. The Loft costs $49.95, but Ninety7 slashed that price tag to $29.95 for Black Friday/Cyber Monday, and it was still available for that price as of press time. When I first pulled this base out of the box, I was surprised by how light and sleek it is. Its simple design seamlessly integrates with the look of the Google Home, with a hard plastic outside and a smooth form that doesn't get in the way of sound quality.

The winds of change are blowing through Silicon Valley--not terribly unusual, granted, considering that something new seems to come out of that part of the world several times a year. But this time, we seem to be witnessing the opening of what promises to be a sea of change in computing: The joining of all our devices--including computers, smartphones, smart homes, and even smart cars--with the virtual synapses of a serviceable artificial intelligence, complete with realistic conversational abilities. We've already seen some earlier attempts at realizing this dream. But, some sassy dialogue notwithstanding, these machine "intelligences" have notorious limitations, something the designers of the newest entrant on the scene, Viv, hope to do away with once and for all. Viv's creators, Dag Kittlaus and Adam Cheyer--who also created Siri, before selling it to Apple--hope that their newest creation will become the portal through which users interface with the internet.

An article by Apple describing how the use machine learning to improve HomePod's signal processing system to remove echo, background noise and separate sound sources.

It seems that we've already seen more than we were ready to VR in video games, IoT in medicine and smart cities being brought to life. We are really close to living in some sort of sci-fi so it's a good idea to have a look at the most possible and promising machine learning and AI trends for the upcoming 2018 and ask ourselves if we are ready for them. Healthcare is one of the biggest and most crucial industries in the world so no wonder it's the one that is heavily using the latest technologies – because it's the matter of life and death. First of all, due to artificial intelligence and work with Big Data, a scientist will soon get the opportunity to prevent certain diseases, like cancer. This can be done by analyzing patient's history and all their records so AI will be able to understand the mechanism of disease, thus enabling doctors to be proactive instead of reacting.

Artificial intelligence (AI) is rapidly transforming everything today, from daily lives to transportation to businesses. Humans have always found the concept of AI very enthralling as is evident from the number of hit sci-fi movies. Scientists and researchers have worked hard on making this technology a norm for the human beings. Enterprises are adopting AI and machine learning (ML) for various use cases, which has risen the demand for AI engines that can be used to develop intelligent applications and tools. Such apps and tools help them automate the repetitive, tedious and difficult tasks that can affect productivity and cost of operation.

Rather than explain what we regret buying in those Black Friday sales, we're taking a forward-thinking approach to the holiday season, and the gifts we're looking to give or receive. Chris Schodt might take the OLED TV dive, while Cherlynn Low is trying (again) to get her parents into a smart home ecosystem. Our TV was the first major purchase my wife and I made. We spent most evenings watching movies at her apartment on the screen of her home-built PC and decided an upgrade was in order. After pooling our savings we bought a 42-inch Element TV for $350. Out of the box, it had two large dark patches in the middle of the screen that I convinced myself would even out with some use.

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don't expect the hype to die down over the next 12 months. The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore. But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators. This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions. AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution but also the "thinking" work – planning, strategizing and making decisions.

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies' information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.

Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking food. People may speak to Amazon Echo during the cooking in order to get the information and situation of the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services, and speaks to people the results that cloud services make by querying the embedded cooking knowledge and achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile application are well-designed and implemented to monitor the temperature of cooking food.