If a school does not meet assessment goals for two consecutive years, by law the district must offer stu dents the opportunity to transfer to a school that is meet ing its goals. Making a choice with such potential impact on a child's future is clearly monumental, yet astonishingly few parents take advantage of the opportunity. Our research has shown that a significant part of the problem arises from issues in information access and information overload, par ticularly for low socioeconomic status families. Thus we have developed an online, content-based recommender sys tem, called SmartChoice. It provides parents with school rec ommendations for individual students based on parents' pref erences and students' needs, interests, abilities, and talents.

The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human-intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine-learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain. These activities engage various stakeholders in the task of identifying and producing an agreed-upon set of requirements that clearly specify the functionality, behavior, and constraints of the proposed system.

Marketers evaluate recommender systems not on their algorithms but on how well the vendor's expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young. As a result, solutions are not fully integrated with other business systems and technology platforms. While the market is retail-focused today, interest and vendor offerings are rapidly expanding to other areas.

Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services.

They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many online e-commerce applications such as Amazon.com, This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas.

This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, 1.71% GOOG, 1.64% search engine for years. Behind the scenes, however, the response to this type of query now leverages an enormous amount of machine-learning technology that Google has spent years and billions of dollars developing, in hopes of being a leader in artificial intelligence. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year.

We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, and citizen science. The 2014 conference continued the tradition with a selection of 7 deployed applications describing systems in use by their intended end users, and 14 emerging applications describing works in progress. This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends.

With so much music readily available, tools that help a user find new, interesting music that matches his or her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening tastes, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing customer involvement program (CIP) data to deliver in-product contextual recommendations to end users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.

In the not so distant future, we will have machines capable not only of storing data, but of thinking, feeling, and being as intelligent as the human being. Following the new trends in the information technology market, such as tracking digital transformation, is a key factor for organizations seeking to remain competitive with the great competition in the market. With increasingly advanced and sophisticated resources, technological innovations have computer machines that promise to facilitate the routine of companies, where intelligent machines can perform their activities in an optimized way. We are talking about the trend of the future, a revolutionary technology – Artificial Intelligence (AI). Artificial Intelligence is already part of our everyday life, but many times we do not even notice it.