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.

The Value of Service Learning Projects in Preparing LIS Students for Public Services Careers in Public Libraries

Abstract: Public libraries are uniquely positioned as ideal places for library and information science students to provide service learning experiences due to the nature of public library services and their diverse clientele. As the education and library and information science literature indicate, experiential education of all kinds, including service learning and fieldwork, can be a beneficial component of any educational experience. A case for service learning will be made through a review of experiential education literature and a case study of a service learning experience at a public library in Austin, Texas. Suggestions for best practices in designing service learning projects are provided.

Developing a long-range and outreach plan for your academic library

Abstract: Are today’s academic libraries poised to serve as essential centers of campus activities? Will they be relegated to the back­ ground of campus life? Will libraries be, at best, ignored collateral support services? What tools are available to help academic libraries reposition themselves to serve as new social commons? How can they secure funding amid competition for scarce resources? Marketing can provide an arsenal of skills to assist academic librarians. The essential marketing document to assist libraries in de­ signing their marketing activities is the mar­ keting and outreach plan. Information used to compile this plan includes best practices at similar institutions, local data on user pref­ erences and suggestions, successful library marketing strategies at other institutions, and an analysis of the strengths, weaknesses, op­ portunities, and threats that challenge and support the library’s plan to position itself as a leading information resource. The structure of the plan outlines out­ reach, media, and marketing strategies for specific target audiences. These target audi­ ences may include students and faculty at the home institution, as well as faculty and students at neighboring institutions and the broader community. The plan is based on delineating goals and measurable, time­cen­ tered objectives. The goals of implementing the plan may include: • increasing general awareness of the library; • showcasing the library’s collections; • increasing traffic in the library as mea­ sured by on­site visits; • increasing patron usage of the library resources as measured by circulation and in­house use; • creating an ongoing excitement or buzz about the library through positive media cov­ erage and hosting of high­profi le events; • increasing funds to support the library’s collection and services; • building outside partnerships, including active and supportive Friends groups; • providing staff and the university com­ munity with up­to­date information about the library; and • instituting a plan for continuous evalu­ ation of marketing efforts.

Tribal libraries, archives, and museums : preserving our language, memory, and lifeways

Abstract: Tribal libraries, archives, and museums are unique settings that balance tribal protocols and infuse their services with expressions of tribal lifeways, from their footprints on the land to their architecture and interior design, institutional names, signage, and Native language promotion. This book offers a collection of articles devoted to tribal libraries and archives and provides an opportunity to share their stories, challenges, achievements, and aspirations to the larger professional community.

The use of volunteers in public libraries: A pilot study

Abstract: Fifty-two public libraries in Illinois were surveyed to test an instrument measuring volunteer use. Ninety-eight percent of the libraries returned completed questionnaires. Results indicate that volunteers are currently used in (57% (34) of the libraries within this group, an increase in the level reported in earlier studies. Demo- graphic data is used to construct profiles of the libraries using volun- teers and those not using volunteers. Survey findings identify rea- sons for using or not using volunteers, list the types of activities that volunteers perform, and describe charactcristics of existing volun- teer programs.

How Do We Learn?: Contributions of Learning Theory to Reference Service and Library Instruction

Abstract: Summary This article focuses on some major contributions from learning theory that impact how adults learn. Following an overview of two major learning theories—behavioral and cognitive theories—the authors discuss four factors that affect learning. These are attention, perception, memory, and contiguity and practice. Other topics presented include prior knowledge effect and transfer of knowledge problems. The article closes with five recommendations for incorporating results from learning theory research into reference service and user instruction.

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.