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.

Professional associations in the libraries, archives, and museums (LAM) area have developed competency documents that provide guidance on the education and training of new professionals. One competency area that remains unresolved but is of increasing importance is the knowledge base we refer to as indigenous ways. Indigenous ways refer to the traditional lifeways, personal and community protocol or etiquette, and beliefs of Native peoples. Knowledge of indigenous ways is essential in assisting LAM professionals in understanding the boundaries, limits, and intersections between cultural protocols of indigenous communities and professional values. This paper reports on preliminary findings from the “Indigenous Ways” project - an Institute of Museum and Library Services (IMLS) funded collaborative planning grant to acquire benchmark data on the level of awareness of indigenous ways among libraries, archives, and museum (LAM) faculty, recent graduates, tribal LAM professionals, and individuals who have served on committees or work groups that have produced recent professional documents on indigenous ways. The goal of the project is to assess awareness and develop recommendations for curricular material that might assist LAM educators in incorporating the concepts around indigenous ways into their graduate classes.

Indigenous ways: assessing the awareness of and potential need for identifying content on indigenous world view in educating Libraries/Archives/Museum (LAM) entry level professionals

National Findings, National Measures: The NCES Survey on Children's Services in Public Libraries and "Output Measures for Public Library Service to Children.".

Purpose




This paper aims to review content provided in an article published in 1993 to determine if there is still a need to provide information services to American Indians living away from their homeland areas.




Design/methodology/approach




The design involved reviewing the content and recommendations provided in the 1993 article and then to update this content by reviewing updated US Census data, a literature review and observed examples based on personal observations and experiences.




Findings




The paper found that there is still a need for services due to increased American Indian population. While more American Indian librarians are employed, there is still a need for all librarians to learn how to serve this population.




Originality/value




While the article reviews content from 1993, it provides original perspectives and adds additional evaluation criteria.

Finding face: building collections to support indigenous identity

The library and information science (LIS) classroom setting for reference education is complex and busy. In a semester-long class, students are taxed with understanding concepts and acquiring skill...

Helping LIS Students Understand the Reference Librarian's Teacher Identity

Article in a column for "The Reference Librarian," Material-Mind-Method: On the Teaching of Reference. This article discusses teaching the catalog as a reference tool.

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Loriene Roy

Papers

Indigenous ways: assessing the awareness of and potential need for identifying content on indigenous world view in educating Libraries/Archives/Museum (LAM) entry level professionals

National Findings, National Measures: The NCES Survey on Children's Services in Public Libraries and "Output Measures for Public Library Service to Children.".

Finding face: building collections to support indigenous identity

Helping LIS Students Understand the Reference Librarian's Teacher Identity

Teaching the Catalog as a Reference Tool