2,430 research outputs found
Twelve Theses on Reactive Rules for the Web
Reactivity, the ability to detect and react to events, is an
essential functionality in many information systems. In particular, Web
systems such as online marketplaces, adaptive (e.g., recommender) systems,
and Web services, react to events such as Web page updates or
data posted to a server.
This article investigates issues of relevance in designing high-level programming
languages dedicated to reactivity on the Web. It presents
twelve theses on features desirable for a language of reactive rules tuned
to programming Web and Semantic Web applications
Privacy, security, and trust issues in smart environments
Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
Improving argumentation-based recommender systems through context-adaptable selection criteria
Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the userÂŽs preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĂa de la ComputaciĂłn; Argentina. Universidad Nacional de Entre RĂos; ArgentinaFil: Gottifredi, SebastiĂĄn. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĂa de la ComputaciĂłn; ArgentinaFil: GarcĂa, Alejandro Javier. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĂa de la ComputaciĂłn; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĂa de la ComputaciĂłn; Argentin
Algorithmic abstractions of âfashion identityâ and the role of privacy with regard to algorithmic personalisation systems in the fashion domain
This paper delves into the nuances of âfashionâ in recommender systems and social media analytics, which shape and define an individualâs perception and self-relationality. Its aim is twofold: first, it supports a different perspective on privacy that focuses on the individualâs process of identity construction considering the social and personal aspects of âfashionâ. Second, it underlines the limitations of computational models in capturing the diverse meaning of âfashionâ, whereby the algorithmic prediction of user preferences is based on individual conscious and unconscious associations with fashion identity. I test both of these claims in the context of current concerns over the impact of algorithmic personalisation systems on individual autonomy and privacy: creating âfilter bubblesâ, nudging the user beyond their conscious awareness, as well as the inherent bias in algorithmic decision-making. We need an understanding of privacy that sustains the inherent reduction of fashion identity to literal attributes and protects individual autonomy in shaping algorithmic approximations of the self
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