User-Centered Evaluation Model for Medical Digital Libraries

Kostkova, Patty; Madle, Gemma

doi:10.1007/978-3-642-03262-2_8

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…There is an increasing trend to blend quantitative and qualitative data within a study to provide a broader and deeper perspective. This approach is called triangulation [24, 46].…”

Section: Criteria Measurement and Analysismentioning

confidence: 99%

“…Furthermore, as mentioned in Section 1, since DLs are destined to serve user communities, the user’s opinion is one of the main aspects to be considered in the quality evaluation of DLs. For these reasons, techniques that require the user’s participation such as interviews and questionnaires are the focus of this review, and the prime methods for collecting qualitative data [8, 18, 25, 31–33, 35, 36, 38–47, 49–53]. In addition, observations in which user actions are recorded are also common methods for obtaining data [23, 24, 28, 63].…”

Section: Criteria Measurement and Analysismentioning

confidence: 99%

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A review of quality evaluation of digital libraries based on users’ perceptions

Heradio¹,

Fernández-Amorós²,

Cabrerizo³

2012

Journal of Information Science

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In the past two decades, the use of digital libraries (DLs) has grown significantly. Accordingly, questions about the utility, usability and cost of DLs have started to arise, and greater attention is being paid to the quality evaluation of this type of information system. Since DLs are destined to serve user communities, one of the main aspects to be considered in DL evaluation is the user’s opinion. The literature on this topic has produced a set of varied criteria to judge DLs from the user’s perspective, measuring instruments to elicit users’ opinions, and approaches to analyse the elicited data to conclude an evaluation. This paper provides a literature review of the quality evaluation of DLs based on users’ perceptions. Its main contribution is to bring together previously disparate streams of work to help shed light on this thriving area. In addition, the various studies are discussed, and some challenges to be faced in the future are proposed.

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“…There is an increasing trend to blend quantitative and qualitative data within a study to provide a broader and deeper perspective. This approach is called triangulation [24, 46].…”

Section: Criteria Measurement and Analysismentioning

confidence: 99%

Section: Criteria Measurement and Analysismentioning

confidence: 99%

A review of quality evaluation of digital libraries based on users’ perceptions

Heradio¹,

Fernández-Amorós²,

Cabrerizo³

2012

Journal of Information Science

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An ontological representation of the digital library evaluation domain

Tsakonas

Papatheodorou

2011

J. Am. Soc. Inf. Sci.

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Digital library evaluation is a complex field, as complex as the phenomena it studies. The interest of the digital library society still remains vibrant after all these years of solidification, as these systems have entered real-life applications. However the community has still to reach a consensus on what evaluation is and how it can effectively be planned. In the present article, an ontology of the digital library evaluation domain, named DiLEO, is proposed, aiming to reveal explicitly the main concepts of this domain and their correlations, and it tries to combine creatively and integrate several scientific paradigms, approaches, methods, techniques, and tools. This article demonstrates the added value features of the ontology, which are the support of comparative studies between different evaluation initiatives and the assistance in effective digital library evaluation planning.

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Modeling User Preferences in Recommender Systems

Jawaheer

Weller

Kostkova

2014

ACM Trans. Interact. Intell. Syst.

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105

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Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users' preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.

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User-Centered Evaluation Model for Medical Digital Libraries

Cited by 3 publications

References 10 publications

A review of quality evaluation of digital libraries based on users’ perceptions

A review of quality evaluation of digital libraries based on users’ perceptions

An ontological representation of the digital library evaluation domain

Modeling User Preferences in Recommender Systems

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