Publikationen

Uptrendz: API-Centric Real-Time Recommendations in Multi-Domain Settings

Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users

In industrial electro galvanizing lines, the performance of the dimensionally stable anodes (Ti +IrOx) is a crucial factor for product quality. Ageing of the anodes causes worsened zinc coatingdistribution on the steel strip and a significant increase in production costs due to a higher resistivityof the anodes. Up to now, the end of the anode lifetime has been detected by visual inspectionevery several weeks. The voltage of the rectifiers increases much earlier, indicating the deteriorationof anode performance. Therefore monitoring rectifier voltage has the potential for a prematuredetermination of the end of anode lifetime. Anode condition is only one of many parameters affectingthe rectifier voltage. In this work we employed machine learning to predict expected baseline rectifiervoltages for a variety of steel strips and operating conditions at an industrial electro galvanizingline. In the plating section the strip passes twelve “Gravitel” cells and zinc from the electrolyte isdeposited on the surface at high current densities. Data, collected on one exemplary rectifier unitequipped with two anodes, have been studied for a period of two years. The dataset consists of onetarget variable (rectifier voltage) and nine predictive variables describing electrolyte, current andsteel strip characteristics. For predictive modelling, we used selected Random Forest Regression.Training was conducted on intervals after the plating cell was equipped with new anodes. Our resultsshow a Normalized Root Mean Square Error of Prediction (NRMSEP) of 1.4 % for baseline rectifiervoltage during good anode condition. When anode condition was estimated as bad (by manualinspection), we observe a large distinctive deviation in regard to the predicted baseline voltage. Thegained information about the observed deviation can be used for early detection resp. classificationof anode ageing to recognize the onset of damage and reduce total operation cost

This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.