Show Notes
In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Technology. Florian is working on Monasterium.net, Europe's largest online collection of historical charters, containing millions of medieval and early modern documents from across the continent. The conversation delves into why traditional recommender systems fall short in the digital humanities space, where users range from expert historians and genealogists to art historians and linguists, each with unique research needs and information-seeking behaviors. Florian explains the technical challenges of building a recommender system for cultural heritage materials, including dealing with sparse user-item interaction matrices, the cold start problem, and the need for multi-modal similarity approaches that can handle text, images, metadata, and historical context. The platform leverages various embedding techniques and gives users control over weighting different modalities—whether they're searching based on text similarity, visual imagery, or diplomatic features like issuers and receivers. A key insight from Florian's research is the importance of balancing serendipity with utility, collection representation to prevent bias, and system explainability while maintaining effectiveness. The discussion also touches on unique evaluation challenges in non-commercial recommendation contexts, including Florian's "research funnel" framework that considers discovery, interaction, integration, and impact stages. Looking ahead, Florian envisions recommendation systems becoming standard tools for exploration across digital archives and cultural heritage repositories throughout Europe, potentially transforming how researchers discover and engage with historical materials. The new version of Monasterium.net, set to launch with enhanced semantic search and recommendation features, represents an important step toward making cultural heritage more accessible and discoverable for everyone.
More from Data Skeptic
The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear. Shimon explores how gesture-sensing earbuds could transform virtual reality and wearable computing through intuitive, user-centered interactions that make technology more accessible and adaptable.
How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior? Kyle speaks with Cornell Tech professor Wendy Ju about implicit interaction, "Wizard of Oz" prototyping, and what studying pedestrians, self-driving cars, and even robotic furniture can teach us about designing technology that behaves the way people expect.
The data we collect about ourselves can tell us a lot—but only if the technology collecting it actually fits into our lives. Daniel Epstein explores personal informatics, from fitness trackers and food journals to baby tracking and AI, and explains why abandoning a tracking tool doesn't necessarily mean it failed.
In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control. From fake reviews and shilling attacks to explainable recommendations and user-selected algorithms, we look at what happens when recommender systems must answer not only for what they recommend, but for the consequences of those choices.
In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, fairness, human curation, embeddings, and the growing role—and risks—of large language models in shaping what gets recommended to us.