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

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering

Hybrid Recommender Systems: Survey and Experiments

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

/pdf/a-survey-of-collaborative-filtering-techniques-19zxbumsgq.pdf

A survey of collaborative filtering techniques

How to ... be a reflective practitioner.

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Library Services to Indigenous Populations : Case Studies

The University of Texas and the Smithsonian National Museum of the American Indian have had experience working together on virtual museum projects that bring indigenous people to the museum for the purpose of "digitally repatriating" important cultural items. The initial virtual museum project provided a strategy for cultural responsive teaching in American Indian schools. They also provided a forum for cultural revitalization and cultural collaboration. The university and the museum are developing proposals to further develop the digital repatriation concept to include more tribal community participation and international indigenous participation.

Digital repatriation: virtual museum partnerships with indigenous peoples

This paper describe a project that involved educators and three Native American communities in the construction of a virtual tour now available on the Web site of the National Museum of the American Indian (http://www.conexus.si.edu/). In fall 1998, the Pueblo of Laguna Department of Education, the College of Education and Graduate School of Library and Information Science at The University of Texas at Austin, and the Smithsonian＊s National Museum of the American Indian (NMAI) began the first collaboration that brought Native American students, teachers, and cultural representatives to the NMAI George Gustav Heye Center in New York City. The virtual tour makes extensive use of QuickTime Virtual Reality (QTVR). The panoramas of the exhibition space serve as an interface for accessing the featured objects selected by the students. Clickingon a hot spot over the museum display of a featured object causes the QTVR object to load in a separate Webpage frame accompanied by an interpreted essay written by a student. Clickable floor plans of the exhibition-space offer another method of navigating the virtual tour and accessing the virtual objects.

https://jlis.glis.ntnu.edu.tw/ojs../index.php/jlis/article/download/396/396

Digital Repatriation: Constructing a Culturally Responsive Virtual Museum Tour

Public libraries across the country design and deliver programs for military veterans and their families, often collaborating with other organizations and agencies. The examples were assembled from both general and specialized interviews with librarians, and literature and Internet searches, with summaries of programs assembled and written by multiple researchers. The specific limitations on the listing in this article are clearly outlined in the first section. Overall, however, the article provides a good introductory overview of what’s going on in veteran’s programs and services in America’s public libraries in over seventy libraries in forty states. We hope this list of what public libraries have organized or are doing will supply other public librarians with ideas on how to provide new services or enhance their existing ones.

Public Library Services for Veterans: Selected Brief Case Studies

For many students, the practice of reference is a cross between completing a crossword puzzle and competing on the television game show Jeopardy Central to this activity is a question that they fe

Loriene Roy

Papers

Library Services to Indigenous Populations : Case Studies

Digital repatriation: virtual museum partnerships with indigenous peoples

Digital Repatriation: Constructing a Culturally Responsive Virtual Museum Tour

Public Library Services for Veterans: Selected Brief Case Studies

Asking Questions: Considering the Patron from Direct Experience