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.

/pdf/toward-the-next-generation-of-recommender-systems-a-survey-4wwsv77rns.pdf

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.

Reference education instruction is compatible with the theories and praxis of feminism. This column features key concepts in feminist theories and provides examples of how current education for fut...

Material-Mind-Method: On the Teaching of Reference Incorporating Feminist Pedagogies in Reference Education

In past columns, I have introduced class assignments in a basic reference class as examples of how to assist graduate students to prepare for future careers as information specialists. In m...

Informing students about assignment evaluation: The rubric

In spring 2016, students enrolled in a face-to-face graduate “Library Instruction and Information Literacy” (LIIL) class in the School of Information (iSchool) at the University of Texas at Austin created a suite of instructional videos for librarians at TCUs to use with their students. Over the past twenty years, librarians at the TCUs have attended a five-day Tribal College Librarians Professional Development Institute (TCLPDI), usually held on the campus of Montana State University in Bozeman, Montana. The iSchool students presented their work at the TCLPDI in June 2016, inviting feedback from patrons including projections on how the videos might be used at TCUs.

The Searching Circle: Library Instruction for Tribal College Students

Purpose




The purpose of this paper is to involve readers in an exercise in conceptualizing the library workplace of the future and how to prepare librarians to be successful in these future scenarios.




Design/methodology/approach




This paper introduces a visualization exercise that invites readers to contemplate the likelihood and desirability of five scenarios for libraries of the future. Readers are also invited to create a new scenario. Readers then can reflect on the skills and aptitudes librarians would need to have or acquire in order to succeed in one of the scenarios. The method involves visualization, reflection, and self-assessment.




Findings




The authors have tested this exercise in in-service training with public, academic, and special librarians as well as with students in graduate library and information science courses. The end result is deep conversation, engagement, and sharing.




Research limitations/implications




The authors have observations on the effectiveness of this future exercise with students and practicing librarians but did not structure its use as a research project. In years of attending conferences and delivering over 600 conference presentations, the authors have yet to observe a similar future exercise being employed.




Practical implications




This exercise promotes reflection and discussion. It enables librarians to consider these scenarios on their own or in groups of varying sizes. It has application in understanding the complexity of futurism and how it might impact their future lives. And it emphases that individual librarians have some degree of independence in how they respond to change and, ultimately, a future much different from the present.




Social implications




Libraries are social institutions and their services reflect and respond to changes in society. Recent changes in governance around the world, warfare, the refugee crisis, and episodes of violence have contributed to tension. These are the times when librarians are called on more than ever to respond. Considering possible and improbable futures and one’s response to those futures are helpful in preparing one’s professional response in these difficult times.




Originality/value




This paper introduces an original model for discussing many possible futures for libraries and for librarians to consider their roles in those futures.

Loriene Roy

Papers

Material-Mind-Method: On the Teaching of Reference Incorporating Feminist Pedagogies in Reference Education

Informing students about assignment evaluation: The rubric

The Searching Circle: Library Instruction for Tribal College Students

Charting a multi-dimensional role of future librarians