Account-based recommenders in open discovery environments

This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others. The approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems. The authors discuss the need for large data sets from which to build an algorithm and introduce a prototype recommender service, describing the prototype’s data flow pipeline and machine learning processes. The browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations; with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account. In the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations.University of Illinois Campus Research Board (RB16001)Ope

Emerging Technologies: Knowledge Panels, AI/ Machine Learning for Data Discovery & Reuse

Panel keynote discussion of Emerging Technologies: "Tomorrow’s technologies are shaping our world today, revolutionizing the way we live and learn. Virtual Reality, Augmented Reality, Artificial Intelligence, Machine Learning, Blockchain, Internet of Things, Drones, Personalization, the Quantified Self. Libraries can and should be the epicenter of exploring, building and promoting these emerging techs, assuring the better futures and opportunities they offer are accessible to everyone. Learn what libraries are doing right now with these cutting-edge technologies, what they’re planning next and how you can implement these ideas in your own organization."Ope

User perspectives on personalized account-based recommender systems

This research is focused on understanding user preferences for "my account"-based recommendations of library content. By interviewing users we have explored user attitudes about three areas of recommendation services; including 1) eliciting preferences for recommendation, 2) displaying recommendations, and 3) revising recommendations based on results. User interviews indicated a need for crafting recommender services in library settings with transparent functionality. Users requested that system designers make clear how recommendations are designed and provided. Further findings indicated a desire to use recommender systems to explore interdisciplinary research domains that have otherwise not been considered.Ope

Students' Growing Concern with Surveillance Capitalism

With research funding from the University of Illinois Campus Research Board, a personalized account-based recommender was developed in the University Library's mobile app interface. The recommender system (RS) was derived from data mining topic clusters of items that are checked out together. Using the library mobile RS as a prompt to understand student preferences for personalized account-based RS, structured interviews were undertaken and analyzed thematically to determine RS features and functionality desired. In the interviews, students described their perceptions of RS, together with features and functionality desired. Students indicated that they desired data stewardship and sharing levels, which provided valuable input into matters of system transparency pertaining to recommendations derived algorithmically. An unexpected finding from students was growing unease with aspects of surveillance capitalism [1]. One student referred to YouTube as an example of a service that did not work the way she wanted it to and noted that "...frequently YouTube doesn't work so good because it gives you a recommendation based on one thing you did. It should be based more on a frequently searched thing. Recommendations are sending you things you already are interested in, which might not show you newer things and that is not really a good way to learn." Students also indicated that they did not like the fact that commerce seems to drive recommenders, for example, "...on the Internet, you might be interested in finding information about something but not want to buy." Another student took a measured approach to this problem, noting "...when they [recommenders] are trying to sell something it feels predatory, but otherwise, it is good." This presentation will explore in greater detail the need to safeguard student privacy despite the finding that students believed that there is a place for RS in academic library settings. Academic library recommenders can distinguish themselves from commercial recommenders in several ways, including increased transparency beyond what is available in commercial systems, and by attending to the level of student privacy desired as a system design issue.University of Illinois Campus Research Board (RB16001)Ope

Perspectivas do uso do aprendizado de máquina em bibliotecas : uma revisão sistemática de literatura

Dissertação (mestrado) — Universidade de Brasília, Faculdade de Estudos Sociais Aplicados, Departamento de Ciência da Informação e Documentação, 2022.O presente trabalho tem por finalidade apresentar as aplicações da Inteligência Artificial, com ênfase em machine learning, em bibliotecas, cujo objetivo principal é mapear benefícios e impactos que o aprendizado de máquina pode oferecer para o desenvolvimento de produtos e serviços em bibliotecas. A fim de atender este objeto, o estudo se pautará em uma pesquisa de caráter qualitativo e quantitativo, com a abordagem exploratória, de natureza pura, por meio do uso da pesquisa bibliográfica. E, para realizar tal investigação, recorre-se à revisão sistemática de literatura, por meio da produção de um protocolo de pesquisa, baseado nas diretrizes propostas por Galvão e Ricarte (2020) para o campo da Ciência da Informação, complementados pelos estudos produzidos por Kitchenham (2004) e Felizardo et al (2017) para o campo da Ciência da Computação. Por fim, conclui-se que este estudo proporciona ao pesquisador refletir e identificar novos fenômenos nas relações interdisciplinares entre a Ciência da Informação e a Inteligência Artificial.The present work aims to present the applications of Artificial Intelligence, with emphasis on machine learning, in libraries, whose main objective is to map benefits and impacts that machine learning can offer for the development of products and services in libraries. In order to meet this object, the study will be based on a qualitative and quantitative research, with an exploratory approach, of a pure nature, through the use of bibliographic research. And, to carry out such an investigation, a systematic literature review is used, through the production of a research protocol, based on the guidelines proposed by Galvão and Ricarte (2020) for the field of Information Science, complemented by studies produced by Kitchenham (2004) and Felizardo et al (2017) for the field of Computer Science. Finally, it is concluded that this study allows the researcher to reflect and identify new phenomena in the interdisciplinary relationships between Information Science and Artificial Intelligence.El presente trabajo tiene como objetivo presentar las aplicaciones de la Inteligencia Artificial, con énfasis en el aprendizaje automático, en las bibliotecas, cuyo principal objetivo es mapear los beneficios e impactos que el aprendizaje automático puede ofrecer para el desarrollo de productos y servicios en las bibliotecas. Para cumplir con este objeto, el estudio se basará en una investigación cualitativa y cuantitativa, con un enfoque exploratorio, de carácter puro, mediante el uso de la investigación bibliográfica. Y, para llevar a cabo esta investigación, se utiliza una revisión sistemática de la literatura, a través de la producción de un protocolo de investigación, basado en las directrices propuestas por Galvão y Ricarte (2020) para el campo de las Ciencias de la Información, complementado con estudios producidos por Kitchenham (2004) y Felizardo et al (2017) para el campo de las Ciencias de la Computación. Finalmente, se concluye que este estudio permite al investigador reflexionar e identificar nuevos fenómenos en las relaciones interdisciplinarias entre las Ciencias de la Información y la Inteligencia Artificial

Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning: Final Report

The generation, dissemination, and analysis of digital information is a significant driver, and consequence, of technological change. As data and information stewards in physical and virtual space, research libraries are thoroughly entangled in the challenges presented by the Fourth Industrial Revolution:1 a societal shift powered not by steam or electricity, but by data, and characterized by a fusion of the physical and digital worlds.2 Organizing, structuring, preserving, and providing access to growing volumes of the digital data generated and required by research and industry will become a critically important function. As partners with the community of researchers and scholars, research libraries are also recognizing and adapting to the consequences of technological change in the practices of scholarship and scholarly communication. Technologies that have emerged or become ubiquitous within the last decade have accelerated information production and have catalyzed profound changes in the ways scholars, students, and the general public create and engage with information. The production of an unprecedented volume and diversity of digital artifacts, the proliferation of machine learning (ML) technologies,3 and the emergence of data as the “world’s most valuable resource,”4 among other trends, present compelling opportunities for research libraries to contribute in new and significant ways to the research and learning enterprise. Librarians are all too familiar with predictions of the research library’s demise in an era when researchers have so much information at their fingertips. A growing body of evidence provides a resounding counterpoint: that the skills, experience, and values of librarians, and the persistence of libraries as an institution, will become more important than ever as researchers contend with the data deluge and the ephemerality and fragility of much digital content. This report identifies strategic opportunities for research libraries to adopt and engage with emerging technologies,5 with a roughly fiveyear time horizon. It considers the ways in which research library values and professional expertise inform and shape this engagement, the ways library and library worker roles will be reconceptualized, and the implication of a range of technologies on how the library fulfills its mission. The report builds on a literature review covering the last five years of published scholarship, primarily North American information science literature, and interviews with a dozen library field experts, completed in fall 2019. It begins with a discussion of four cross-cutting opportunities that permeate many or all aspects of research library services. Next, specific opportunities are identified in each of five core research library service areas: facilitating information discovery, stewarding the scholarly and cultural record, advancing digital scholarship, furthering student learning and success, and creating learning and collaboration spaces. Each section identifies key technologies shaping user behaviors and library services, and highlights exemplary initiatives. Underlying much of the discussion in this report is the idea that “digital transformation is increasingly about change management”6 —that adoption of or engagement with emerging technologies must be part of a broader strategy for organizational change, for “moving emerging work from the periphery to the core,”7 and a broader shift in conceptualizing the research library and its services. Above all, libraries are benefitting from the ways in which emerging technologies offer opportunities to center users and move from a centralized and often siloed service model to embedded, collaborative engagement with the research and learning enterprise