Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wireless sensor networks for healthcare: A survey

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

by J. Biggs and C. Tang, Maidenhead, England,
Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Teaching for quality learning at university

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A literature review and classification of recommender systems research

This paper presents the prediction of TV station ratings considering the user's profile of its audience, received from their settop boxes. It is based on a TV content recommender for groups that uses multidimensional classifications following the TV-Anytime approach. Two applications are presented in order to maximize ratings: an algorithm to configure the TV program schedule of a TV station, and another one that decides in real time between a set of contents which one a TV station should broadcast in a certain moment to increase the audience.

Prediction of TV-station ratings based on content recommenders

The possibility of transmitting interactive applications along with the audiovisual contents will turn the television into a universal platform to access the Information Society. Nowadays, there is growing interest in exploiting that potential to develop distance learning policies, to supplement the facilities offered by the current e-learning technologies. This short paper describes the implementation of a software module that enables the provision of educational services over Interactive Digital TV (t-learning). This module has been designed according to the technical peculiarities of an IDTV receiver, and resorts to existing standards to favor the convergence of all distance learning mediums.

A Core of Standards to Support T-learning

The interactions enabled by the popular sites of the Web 2.0 are largely confined to the virtual world of the Internet, thus failing to engage people in relevant interactions with people, contents or resources in their physical environment. In this paper, we motivate the potential of automatically establishing sporadic social networks among people (acquaintances or strangers) who happen to be physically close to one another at a certain moment. We present the design of one platform intended to provide solutions from the lowest level of establishing ad-hoc connections among nearby mobile devices, up to the highest level of automatically identifying the most relevant pieces of information to deliver at any time. One specific scenario is discussed for museum environments, considering the possibility of engaging groups of people into immersive collective experiences to learn about major historical events.

Leveraging Short-Lived Social Networks in Museums to Engage People in History Learning

T-learning – the provision of educational services over Interactive Digital TV – is regarded as a complement to e-learning solutions, whose scope is limited due to the digital divide. The increasing connectivity, together with the fact that IDTV users are no longer passive spectators, permits to start thinking on highly interactive services that support fluid communication among users and service providers. This may enhance the prospects of learning at home, as long as the current offer is mostly based on broadcast content, with scarce use of return channels for feedback. This paper proposes an extension to the Multimedia Home Platform standard, aimed at providing better support for collaborative learning services. We describe an architecture for such services and a selection of freely available technologies for their implementation. We also discuss the possible market implications of our approach, as the ideas presented here contribute to openness in the field of IDTV services.

Technologies to Support Collaborative Learning over the Multimedia Home Platform

Electronic Health Records (EHR) are a crucial element towards the implantation of information technologies in healthcare. One of the goals pursued with these artifacts is to prevent medicine misuse, for which EHR standards define fields to record medical prescriptions and medication regimens. Unfortunately, the information stored in an EHR about how and when the patient does take his/her medicines is most often imprecise and incomplete, which implies severe health risks and brings down the benefits of technology. There exist solutions to get accurate records from inpatient settings (i.e. when the patient is treated in hospital), but not from contexts of daily life (e.g. when the patient takes medicines in home or at work) even though these are breeding ground for medication misuse. In this paper, we present an approach to fill in this gap, building on a system that monitors medicine intake from within a residential network, and relying on European standards for the storage and exchange of health-related information.

Yolanda Blanco-Fernández

Papers

Prediction of TV-station ratings based on content recommenders

A Core of Standards to Support T-learning

Leveraging Short-Lived Social Networks in Museums to Engage People in History Learning

Technologies to Support Collaborative Learning over the Multimedia Home Platform

Entering information about medication intake in standard Electronic Health Records from the networked home