21,983 research outputs found
Fairness in Information Access Systems
Recommendation, information retrieval, and other information access systems
pose unique challenges for investigating and applying the fairness and
non-discrimination concepts that have been developed for studying other machine
learning systems. While fair information access shares many commonalities with
fair classification, the multistakeholder nature of information access
applications, the rank-based problem setting, the centrality of personalization
in many cases, and the role of user response complicate the problem of
identifying precisely what types and operationalizations of fairness may be
relevant, let alone measuring or promoting them.
In this monograph, we present a taxonomy of the various dimensions of fair
information access and survey the literature to date on this new and
rapidly-growing topic. We preface this with brief introductions to information
access and algorithmic fairness, to facilitate use of this work by scholars
with experience in one (or neither) of these fields who wish to learn about
their intersection. We conclude with several open problems in fair information
access, along with some suggestions for how to approach research in this space
Towards personalization in digital libraries through ontologies
In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the
elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment
Adaptation âin the Wildâ: Ontology-Based Personalization of Open-Corpus Learning Material
Abstract. Teacher and students can use WWW as a limitless source of learning material for nearly any subject. Yet, such abundance of content comes with the problem of finding the right piece at the right time. Conventional adaptive educational systems cannot support personalized access to open-corpus learning material as they rely on manually constructed content models. This paper presents an approach to this problem that does not require intervention from a human expert. The approach has been implemented in an adaptive system that recommends students supplementary reading material and adaptively annotates it. The results of the evaluation experiment have demonstrated several significant effects of using the system on students â learning
Adaptation âin the Wildâ: Ontology-Based Personalization of Open-Corpus Learning Material
Teacher and students can use WWW as a limitless source of learning material for nearly any subject. Yet, such abundance of content comes with the problem of finding the right piece at the right time. Conventional adaptive educational systems cannot support personalized access to open-corpus learning material as they rely on manually constructed content models. This paper presents an approach to this problem that does not require intervention from a human expert. The approach has been implemented in an adaptive system that recommends students supplementary reading material and adaptively annotates it. The results of the evaluation experiment have demonstrated several significant effects of using the system on studentsâ learning.\u
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