25 citations
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16 papers · 1 filter
Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
Peter Muellner, Anna Schreuer, Simone Kopeinik +2
Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms of…
A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives
Florian Atzenhofer-Baumgartner, Georg Vogeler, Dominik Kowald
This paper presents the first multistakeholder approach for translating diverse stakeholder values into an evaluation metric setup for Recommender Systems (RecSys) in digital archi…
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Andrea Forster, Simone Kopeinik, Denic Helic +2
Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popula…
Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
Robin Ungruh, Alejandro Bellogín, Dominik Kowald +1
Children are often exposed to items curated by recommendation algorithms. Yet, research seldom considers children as a user group, and when it does, it is anchored on datasets wher…
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Kevin Innerebner, Dominik Kowald, Markus Schedl +1
Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processe…
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
Oleg Lesota, Jonas Geiger, Max Walder +2
Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training dat…