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.

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A survey of collaborative filtering techniques

How to ... be a reflective practitioner.

Digital technology to empower indigenous culture and education

To help our graduates become successful in both traditional and nontraditional information settings, Roy and Hallmark suggest that the requirements listed in modern job descriptions reveal the type of skills and talents we should include in our information science curriculum. This unique perspective allows us to determine how well our information science curriculum matches the skillsets required for atypical “cool jobs” not frequently associated with the library and information science field. They offer suggestions on how to include activities and challenges in a variety of courses to make the instructional setting more closely align with future employment settings. Loriene Roy has taught library instruction and information literacy for more than twenty years, and served as ALA president from 2007 to 2008. Elizabeth Hallmark graduated from the iSchool at The University of Texas at Austin. Hallmark herself has “cool job” experience as the Creative Director for the Texas Comptroller of Public Accounts and more recently as Internal Communications Leader in the public medical sector. Together, these authors summarize findings and offer recommendations for preparing students to become successful in those unusual, atypical work settings—those “cool jobs.” —Editor

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Cool Jobs: Expanding the Place of the Library Instruction Curriculum to Assist Graduates in Preparing Students for Atypical Work Environments

items. They make these items available to public view in exhibitions in specially designed architectural spaces, and more recently, in the virtual spaces provided on the World Wide Web and on CD-ROM. By making their collections accessible to the public in carefully crafted and coherent presentations, museums serve an important mission of perpetuating cultural heritage through the educational experiences they offer to the public. Schools share a mission of cultural preservation with museums, making them natural partners in the development of effective educational experiences for young citizens. The creation of virtual museums as classroom learning projects is one emerging strategy schools have explored that makes use of new digital media, the World Wide Web and multimedia authoring. This paper presents a culturally responsive emerging model for school-museum collaboration. The Four Directions Project has been working with American Indian Schools to explore the uses of technology for culturally responsive teaching. One approach Four Directions is exploring is school-museum collaboration for student-created virtual museum projects. A Four Directions Model for school-museum partnerships has emerged from these experiences. Two example projects are described and the benefits of virtual museum projects are discussed. (Contains 11 references.) (AEF) Museums have long been the repository of important cultural Reproductions supplied by EDRS are the best that can be made from the original document. Virtual Museums from Four Directions: An Emerging Model for School-Museum Collaboration / U S DEPARTMENT OF EDUCATION Office of Educational Research and Improvement ' EDUCATIONAL RESOURCES INFORMATION I CENTER (ERIC) This document has been reproduced as ' received from the person or organization originating it 0 Minor changes have been made to improve reproduction quality Mark Christal The University of Texas at Austin Learning Technology Center United States mark@teachnet.edb.utexas.edu Marty Kreipe de Montano Smithsonian National Museum of the American Indian United States demontanom@nmaicrc.si.edu Paul Resta The University of Texas at Austin Learning Technology Center United States resta@mail.utexas.edu Points of view or opinions stated in this ' document do not necessarily represent , official OERl position or policy. Loriene Roy The University of Texas at Austin Graduate School of Library Information Science United States loriene@gslis.utexas.edu rn rl c\1 rl c\1 2 n \ PERMISSION TO REPRODUCE AND ' DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY

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Virtual Museums from Four Directions: An Emerging Model for School-Museum Collaboration

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Creating a Virtual Tour of the American Indian.

Tribal archives are an essential resource for documenting cultural history. This article explores the essential archival functions of a tribal repository and addresses special concerns tribal archivists may face in managing these unique institutions. Tribal archives are growing in number, sparking new initiatives for training and collaboration among those tribes that house archives. As more and more documentation becomes available in electronic form, more repositories feel the pressure to contribute material. Electronic media and the Internet pose a number of concerns and possibilities for tribal archives and tribes as a whole. Funding is perhaps the most important factor in supporting digitization projects. There are a number of ways that archives, libraries and museums of tribes can obtain financial support. Funding agencies such as the Institute of Museum and Library Services (IMLS), the National Park Service, and the National Historical Publications and Records Commission all administer both competitive and non‐competitive grants in support of museums, libraries and archives.

Loriene Roy

Papers

Digital technology to empower indigenous culture and education

Cool Jobs: Expanding the Place of the Library Instruction Curriculum to Assist Graduates in Preparing Students for Atypical Work Environments

Virtual Museums from Four Directions: An Emerging Model for School-Museum Collaboration

Creating a Virtual Tour of the American Indian.

Perspectives on tribal archives