Toward Adaptive Trust Calibration for Level 2 Driving Automation

Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload dynamics of human behavior in an action-automation context. We specifically consider hands-off Level 2 driving automation in a city environment involving multiple intersections where the human chooses whether or not to rely on the automation. We consider automation reliability, automation transparency, and scene complexity, along with human reliance and eye-gaze behavior, to model the dynamics of human trust and workload. We demonstrate that our model framework can appropriately vary automation transparency based on real-time human trust and workload belief estimates to achieve trust calibration.Comment: 10 pages, 8 figure

Tell Me Why (I Want It That Way) – Effects of Explanations and Online Customer Reviews on Trust in Recommender Systems

Review-based recommender systems (RS) have shown great potential in helping users manage information overload and find suitable items. However, a lack of trust still impedes the widespread acceptance of RS. To increase users’ trust, research proposes various methods to generate justifications or explanations. Furthermore, online customer reviews (OCRs) are found to be a trustworthy and reliable source of information. However, it is still unclear how justifications compare to explanations in their influence on users’ trust and whether basing them on OCRs additionally adds trust. Hence, we conduct an online experiment with 531 participants and find that explanations exceed justifications in increasing users’ trust, while basing them on OCRs directly increases users’ intentions to use the system and adopt recommendations without increasing trust in the RS themselves. Unifying different research streams from review-based RS and Explainable Artificial Intelligence, we provide an overarching, holistic view on the conception of justifications and explanations

Recommender-based enhancement of discovery in Geoportals

In many cases web search engines like Google are still used for discovery of geographic base information. This can be explained by the fact that existing approaches for Geo-information retrieval still face significant challenges. Discovery in currently available Geoportals is usually restricted to text-based search based on keywords, title and abstract as well as applying spatial and temporal filters. Furthermore, user context as well as search results of other users are not incorporated. In order to improve the quality of search results we propose to extend the suitable searching matches in Geoportals with user behaviour and to present them as non-directly linked recommendations like in e.g. Amazon's “Customers Who Bought This Item Also Bought” approach. As shown in the proof-of-concept EU FP7 EnerGEO Geoportal, it guarantees results that are not in the data itself but rather derived from the context of other users’ searches and views

Recommender-based enhancement of discovery in Geoportals

Abstract In many cases web search engines like Google are still used for discovery of geographic base information. This can be explained by the fact that existing approaches for Geo-information retrieval still face significant challenges. Discovery in currently available Geoportals is usually restricted to text-based search based on keywords, title and abstract as well as applying spatial and temporal filters. Furthermore, user context as well as search results of other users are not incorporated. In order to improve the quality of search results we propose to extend the suitable searching matches in Geoportals with user behaviour and to present them as non-directly linked recommendations like in e.g. Amazon's "Customers Who Bought This Item Also Bought" approach. As shown in the proof-of-concept EU FP7 EnerGEO Geoportal, it guarantees results that are not in the data itself but rather derived from the context of other users' searches and views

An Empirical Study on Consumer Behavior in the Interaction with Knowledge-based Recommender Applications

Knowledge-based recommender technologies provide a couple of mechanisms for improving the accessibility of product assortments for customers, e.g., in situations where no solution can be found for a given set of customer requirements, the recommender application calculates a set of repair actions which can guarantee the identification of a solution. Further examples for such mechanisms are explanations or product comparisons. All these mechanisms have a certain effect on the behavior of customers interacting with a recommender application. In this paper we present results from a user study, which focused on the analysis of effects of different recommendation mechanisms on the overall customer acceptance of recommender technologies

Sistemas de recomendación para webs de información sobre la salud

La presente tesis de máster representa un estado del arte de los sistemas de recomendación en el hábito computacional. Mediante un profundo estudio de la literatura se ha desarrollado un análisis de los diferentes sistemas de recomendación existentes así como su clasificación, bondades y defectos. Este estudio del estado del arte de los sistemas de recomendación se ha llevado a cabo con el fin de obtener una idea clara de las posibles soluciones (sistemas de recomendación) a implementar para un proyecto del “Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED)” llamado “Calidad en los sitios del área de la salud”. El proyecto “calidad en los sitios del área de la salud” consiste en la creación de una aplicación web dedicada a temas de salud