UMAP_latebreaking'15 Accepted Papers with Abstracts
Personality & Emotional States:
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Giang Binh Tran and Eelco Herder. Detecting Filter Bubbles in Ongoing News StoriesAbstractIn this paper, we analyze differences in perspective between timelines created by news agencies from various countries. By employing methods for date and headline selection, which have been extensively evaluated in previous work, we show several types of bias that exist in the media landscape. As users typically select only a small number of news sources to follow, they necessarily experience at least some `tunnel vision', as commonly associated with the so-called `filter bubble'. By recognizing and emphasizing the peculariaties of the users' self-selected news sources, we can help them to break out of the bubble. |
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Markus Rokicki, Eelco Herder and Elena Demidova. What's On My Plate:
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Stephan Weibelzahl, Dominikus Heckmann, Eelco Herder, Karsten Müssig and Janko Schildt. Adaptive Recommendations for Patients with DiabetesAbstractDiabetes mellitus is a major epidemic with about 8.3% of the world population being affected. Proper treatment minimizes the risk of secondary diseases. The GlycoRec system aims to support patients in making decision that are related to the treatment by modeling their behavior and their physiology. Here we describe the aims and first steps towards the development of GlycoRec. |
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Marwan Al-Tawil, Vania Dimitrova and Dhavalkumar Thakker. Using Basic Level Concepts in a Linked Data Graph to Detect User's Domain FamiliarityAbstractWe investigate how to provide personalized nudges to aid a user’s exploration of linked data in a way leading to expanding her domain knowledge. This requires a model of the user’s familiarity with domain concepts. The paper examines an approach to detect user domain familiarity by exploiting anchoring concepts which provide a backbone for probing interactions over the linked data graph. Basic level concepts studied in Cognitive Science are adopted. A user study examines how such concepts can be utilized to deal with the cold start user modelling problem, which informs a probing algorithm. |
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Joseph Jay Williams and Neil Heffernan. A Methodology for Discovering how to Adaptively Personalize to Users using Experimental ComparisonsAbstractWe explain and provide examples of a formalism that supports the methodology of discovering how to adapt and personalize technology by combining randomized experiments with variables associated with user models. We characterize a formal relationship between the use of technology to conduct A/B experiments and use of technology for adaptive personalization. The MOOClet Formalism [7] captures the equivalence between experimentation and personalization in its conceptualization of modular components of a technology. This motivates a unified software design pattern that enables technology components that can be compared in an experiment to also be adapted based on contextual data, or personalized based on user characteristics. With the aid of a concrete use case, we illustrate the potential of the MOOClet formalism for a methodology that uses randomized experiments of alternative micro-designs to discover how to adapt technology based on user characteristics, and then dynamically implements these personalized improvements in real time. |
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