YouTube's T.J. Fowler explains how the Google Assistant can make better music suggestions on YouTube's revamped Music service A link has been sent to your friend's email address. A link has been posted to your Facebook feed. YouTube's T.J. Fowler explains how the Google Assistant can make better music suggestions on YouTube's revamped Music service USA TODAY

Recommender system has been demonstrated as a successful solution to assist decision makings. Context-awareness becomes necessity in recommendations, especially in mobile computing, since a user's decision may vary from contexts to contexts. Context-aware recommender systems, therefore, emerged to adapt the personalizations to different contextual situations. Context filtering is one of the popular ways to develop the context-aware recommendation models. Contextual pre-filtering techniques have been well developed, but the post-filtering methods are still under investigated. In this paper, we propose a simple but effective post-filtering recommendation approach. We demonstrate the effectiveness of this algorithm in comparison with other context-aware recommendation approaches based on the real-world rating data from mobile applications. Our experimental results reveal that the proposed algorithm is the best post-filtering approach, and it is even able to outperform the popular pre-filtering and contextual modeling recommendation models.

One of the most significant contributions to decision technology is multi-attribute utility (MAU) theory. MAU has gained increased traction in determining the value of information in tactical networking, has been a inspiration for some content-based recommender systems, and artifacts of MAU can be found on nearly every e-commerce website. While recommender systems attempt to create a model of the user (often on latent variables) from rating data, MAU attempts to solicit content-relevant attribute weightings explicitly. Both of these methods have trade-offs which might be mitigated if they could be combined. This research presents a method that we call MAUSVR for fusing recommender and MAU decision technology by automatically learning MAU models (from a user's ratings. A comparison with collaborative filtering techniques on the MovieLens dataset suggests that MAUSVR achieves better ranking quality under sparse conditions while also gaining in transparency and locality. Additionally, MAUSVR was able to be built instantaneously (< 100ms) for more than 75% of the evaluated users with an off-the shelf Java implementation of SMOreg. These findings indicate promise for the use of MAUSVR in real-time decision support systems operating in sparse data conditions.

The success of a recommender system lies in capturing preferences of users and recommending products that best cater to their needs. We restrict our focus to knowledge based recommender systems where we have the flexibility to model users preferences on individual features of the product. In this work, along with learning users preferences, we bring in the idea of looking at the problem of recommending from the manufacturer's point of view. We model prospective buyers of each product in the domain and use this information in predicting products that would potentially be of interest to a given user.

Recommender systems are being used to suggest products to customers, provide personalized product information, and even to provide product reviews. ese systems recommend items among a huge number of possibilities according to users' interests. Recommender systems have al- so been proposed to support the information selection and decision-making processes on e-com- merce web sites. is is the fourth recommender systems special track running in parallel with FLAIRS. The goal of this new special track has been to provide a forum for researchers and practitioners to share their e orts in addressing current issues, challenges, novel approaches, and applica- tions within the broad scope of recommender systems. We continue to aim to cover a wide variety of research areas where recommender systems may be researched and applied.

This paper presents a reading recommendation system based on morpho-phonological, lexical, and syntactic features reflecting both textual complexity and the learner’s linguistic proficiency. The goal of this system is to optimize the reading process of ESL learners by proposing the fittest text to their needs given their incrementally built profile (weighted history of read texts). Fifteen features out of an initial pool of 90 candidates were selected. A corpus of 5052 texts of different levels was collected and used to build the system. To make the system more adaptive, a Progress Rate (PRate) measure was also proposed and integrated into the search process. Finally, the evaluation of the system showed positive results.

The popularity of Amazon's Echo smart speakers has helped push the audience for digital radio past that of FM and AM in the UK for the first time. The milestone, which was reached in the first quarter of this year, could prompt the government to launch a review to evaluate whether it should switch off the FM signal. Digital, which covers listening via digital audio broadcasting (DAB) sets in homes and cars, televisions and through services such as Echo, hit a record share of 50.9% of all radio listening in the three months to March. Amazon's smart speakers, powered by the virtual assistant Alexa, have helped reinvent the medium for a new, tech-savvy generation, many of whom have failed to embrace traditional radio listening. Listening online and via apps proved to be by far the fastest-growing segment of digital consumption, with hours of listening in the first quarter surging by 14m (17%) year on year to 95m hours.

Artificial intelligence has gone from the imagination of people like Philip K. Dick and Arthur C. Clarke, and is now a part of every aspect of technology. The future of smartphones revolves around terminologies like machine learning, artificial intelligence and augmented reality. We're starting to see this happen already, as most smartphone manufacturers now stress that their devices have AI baked in. But is the hype justified, or are we hearing about AI now because the hardware seems to have reached a plateau? What's clear is that the next revolution lies in software, in bringing actual intelligence to "smart" phones, and that's why AI has to be implemented at all stages of the smartphone experience.

Audio Analytic's CEO Chris Mitchel smashes window panes with a sledge hammer to help teach machines how to hear the sound of breaking glass as well as humans. Amazon's Echo speaker knows that you only have to say "Alexa" to wake it up. But technically it can't tell the difference between two people murmuring in the corner of the room and the sound of radio static. That would take a deeper dive into the building blocks of sound itself. But one small startup in Cambridge, UK has spent ten years building up an entirely new language of sound which, for the first time, will allow machines to recognize the sound of human speech.

We are rapidly moving toward a workplace where people interact with machines on a routine basis. With the advent of artificial intelligence (AI), and the chatbots that it powers, technology is now interwoven into many of our everyday job tasks. In fact, it has been reported that more than 80 percent of businesses plan to be using chatbots by 2020. Over the past few years, technology has made huge advances in approximating human interaction, especially when it comes to speech recognition and detection of emotions, visual cues and voice intonation. From Alexa to Siri to Google Home, these technologies are becoming more ubiquitous at a tremendous pace and will have a big impact on how we perform our jobs in the years to come.