scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
Proceedings ArticleDOI
01 Jun 2000
TL;DR: This work describes a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization and shows initial experimental results demonstrate that this approach can produce accurate recommendations.
Abstract: Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

1,330 citations

Posted Content
TL;DR: The authors proposed a content-based book recommendation system that utilizes information extraction and a machine-learning algorithm for text categorization, which has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations.
Abstract: Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

1,268 citations

Journal ArticleDOI
TL;DR: This column introduces the dilemma of that seemingly easy task of teaching reference, and includes examples of course assignments to aid in that effort.
Abstract: Reference is the balance of employing a process to use materials to locate information. A key feature of teaching reference is to help students understand what a reference source is. This column introduces the dilemma of that seemingly easy task. It includes examples of course assignments to aid in that effort.

742 citations

Journal ArticleDOI
TL;DR: For example, if I Can Read, I Can Do Anything (IFI CAN DO ANYTHING) is a national reading program for Native children as mentioned in this paper, which is based on the Spectrum Initiative of the American Library Association.
Abstract: Summary Graduate schools of library and information science (LIS) are rediscovering civic engagement. Examples illustrate how LIS students further diversity efforts through service-based learning experiences. LIS students involved in the American Library Association's Spectrum Initiative helped plan the leadership institute and the longitudinal study of scholars. Students prepared pathfinders for faculty at tribal community colleges and tribal schools. LIS students helped develop and operate “If I Can Read, I Can Do Anything,” a national reading program for schools serving Native children. LIS students helped create a virtual tour of the National Museum of the American Indian (NMAI).

45 citations

Journal ArticleDOI
TL;DR: In this paper, a literature review shows the realm of Indigenous cultural heritage preservation within libraries is an area still ripe for meaningful exploration and achievement, yet this field is also still sensitive and potentially harmful for the cultural communities who have entrusted these institutions with their living treasures.
Abstract: This literature review shows the realm of Indigenous cultural heritage preservation within libraries is an area still ripe for meaningful exploration and achievement. Yet this field is also still sensitive and potentially harmful for the cultural communities who have entrusted these institutions with their living treasures. Opportunities abound to make a difference, but they may need to evolve from changes in generational attitudes and approaches.

23 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: 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.
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.

9,873 citations

Journal ArticleDOI
TL;DR: 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, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
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

3,883 citations

Journal ArticleDOI
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
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.

3,406 citations

Journal Article

3,099 citations