Loriene Roy

Bio: Loriene Roy is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Indigenous & Information literacy. The author has an hindex of 12, co-authored 99 publications receiving 3343 citations.

Incorporating Social Justice in Reference Education

Abstract: Students of reference service can better prepare for careers in serving today’s communities by not only being sensitive to the needs of all members of their communities but also serving as advocate...

Developing a Reading Community: Involving LIS Students in Planning a Readers’ Advisory Event

Abstract: Graduate students in an elective Readers' Advisory class created a variation of a one book–one community event. The event provided a forum for discussing issues related to technology and the future and how it relates to the evolving mission of the public library. Students gained experience in project management and reflected on how they expanded their readers' advisory skills. Future events could be improved with more careful task assignment, better marketing, and better timing.

Answering Questions: Incorporating the Human Element Into Reference Librarianship Education

Abstract: In the November/December issue of American Libraries, Dr. Joe Janes gives a “fond farewell to the Internet Public Library,” which he created in 1995 when he was on the faculty at the University of Michigan (Janes, 2014, p. 27). For many library and information science (LIS) students and their educators, the Internet Public Library (IPL) provided a sound and safe approach to meeting members of the public who expressed their real needs by posting questions online. IPL, now ipl2, provided a structure for students to test and experience the reference techniques that they read about through high quality reference service in real life and real time. Patrons learned to share not only their questions but also how they would use the information they received, what sources they had already consulted, and whether their question was related to a class assignment. Thus, the IPL was both a training tool and an advocacy tool for demonstrating to the public the benefits of working with an information specialist. Janes’ announcement has prompted me to consider how I have incorporated answering questions in the classes that I have taught. While reference service is often blended now with instruction and reader’s advisory, the process of answering questions is both a traditional and contemporary service offered by many libraries. Evidence of this service

Information literacy: key to an inclusive society: 4th European Conference, ECIL 2016, Prague, Czech Republic, October 10-13, 2016 : revised selected papers

Abstract: The relationship of student teachers’ attitudes towards disability, social inclusion and technology’s role in that process was investigated in this study. Results are situated in the context of current literature on social inclusion in the modern technological society, emphasizing people’s ability to use tech‐ nology in order to engage in meaningful social practices. The student teachers’ attitudes towards social inclusion, perceived information literacy, self-efficacy, and perceived attributes of information and communication technologies (ICT) usage were the focus of this survey study. The questionnaire was administered to 300 future teachers studying at the graduate level in Croatia’s university system (University of Zagreb and University of Split). The data collected from the repre‐ sentative sample indicated a high correlation between student teachers’ attitudes towards disability and social inclusion, the self-assessment of information literacy and perceived attributes of ICT access and usage.

Machine learning

Abstract: 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.).

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

Abstract: 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.

Hybrid Recommender Systems: Survey and Experiments

Abstract: 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

A survey of collaborative filtering techniques

Abstract: 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.