2,751 research outputs found
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanaciĂł de persones (PRS) sĂłn un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanaciĂł de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecĂ rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definiciĂł d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funciĂł de la naturalesa de la informaciĂł necessĂ ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procĂ©s, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implĂcita per tal de caracteritzar als usuaris des de mĂşltiples perspectives i punts de vista multidimensionals sense la influència explĂcita de l’usuari. Això s’aplica al problema d'assignaciĂł d’avaluadors per a articles de conferències. Es presta especial atenciĂł al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecĂ rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les Ă rees d’interès per a caracteritzar les persones es dedueixen dels seus currĂculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explĂcitament a les persones. El sistema s'aplica a un problema de selecciĂł de personal on es posa èmfasi en les preferències del candidat que condueixen a un procĂ©s d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informaciĂł implĂcita i atributs indicats explĂcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que mantĂ© la informaciĂł original del conjunt de dades del clĂşster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les Ă rees d'aplicaciĂł futures i noves perspectives.Postprint (published version
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanaciĂł de persones (PRS) sĂłn un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanaciĂł de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecĂ rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definiciĂł d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funciĂł de la naturalesa de la informaciĂł necessĂ ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procĂ©s, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implĂcita per tal de caracteritzar als usuaris des de mĂşltiples perspectives i punts de vista multidimensionals sense la influència explĂcita de l’usuari. Això s’aplica al problema d'assignaciĂł d’avaluadors per a articles de conferències. Es presta especial atenciĂł al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecĂ rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les Ă rees d’interès per a caracteritzar les persones es dedueixen dels seus currĂculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explĂcitament a les persones. El sistema s'aplica a un problema de selecciĂł de personal on es posa èmfasi en les preferències del candidat que condueixen a un procĂ©s d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informaciĂł implĂcita i atributs indicats explĂcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que mantĂ© la informaciĂł original del conjunt de dades del clĂşster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les Ă rees d'aplicaciĂł futures i noves perspectives
Archives, Access and Artificial Intelligence: Working with Born-Digital and Digitized Archival Collections
Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets
Archives, Access and Artificial Intelligence
Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets
From Anecdotal Evidence to Quantitative Evaluation Methods:A Systematic Review on Evaluating Explainable AI
The rising popularity of explainable artificial intelligence (XAI) to
understand high-performing black boxes, also raised the question of how to
evaluate explanations of machine learning (ML) models. While interpretability
and explainability are often presented as a subjectively validated binary
property, we consider it a multi-faceted concept. We identify 12 conceptual
properties, such as Compactness and Correctness, that should be evaluated for
comprehensively assessing the quality of an explanation. Our so-called Co-12
properties serve as categorization scheme for systematically reviewing the
evaluation practice of more than 300 papers published in the last 7 years at
major AI and ML conferences that introduce an XAI method. We find that 1 in 3
papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate
with users. We also contribute to the call for objective, quantifiable
evaluation methods by presenting an extensive overview of quantitative XAI
evaluation methods. This systematic collection of evaluation methods provides
researchers and practitioners with concrete tools to thoroughly validate,
benchmark and compare new and existing XAI methods. This also opens up
opportunities to include quantitative metrics as optimization criteria during
model training in order to optimize for accuracy and interpretability
simultaneously.Comment: Link to website added: https://utwente-dmb.github.io/xai-papers
Archives, Access and Artificial Intelligence
Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
Synthesizing Qualitative Evidence: A Roadmap for Information Systems Research
Qualitative synthesis research is an approach that consolidates the output of different qualitative studies to create new subject knowledge. Such work can help reveal more powerful explanations than that seen in a single study, thereby generating increased levels of understanding of a given phenomenon and greater research finding generalizability. Based on a review of the literature and a survey of qualitative researchers, we found that the information systems (IS) domain lacks a clear understanding of qualitative synthesis methods and, as a result, has largely failed to take advantage of this powerful, high-potential methodological opportunity. To address this shortcoming, this paper is the first to provide a rigorous overview of the full suite of 35 qualitative synthesis methods, as well as guidelines that include a three-tiered selection framework. By using the guidelines and framework in tandem, IS researchers are able to select the qualitative synthesis method most appropriate for a given research study, particularly when the research objective involves knowledge integration/aggregation, interpretation/theory development, and/or informing IS practice
A survey on bias in machine learning research
Current research on bias in machine learning often focuses on fairness, while
overlooking the roots or causes of bias. However, bias was originally defined
as a "systematic error," often caused by humans at different stages of the
research process. This article aims to bridge the gap between past literature
on bias in research by providing taxonomy for potential sources of bias and
errors in data and models. The paper focus on bias in machine learning
pipelines. Survey analyses over forty potential sources of bias in the machine
learning (ML) pipeline, providing clear examples for each. By understanding the
sources and consequences of bias in machine learning, better methods can be
developed for its detecting and mitigating, leading to fairer, more
transparent, and more accurate ML models.Comment: Submitted to journal. arXiv admin note: substantial text overlap with
arXiv:2308.0946
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