Designing Recommender Systems for Digital Humanities
Data Skeptic23 Nov 2025

Designing Recommender Systems for Digital Humanities

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.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(608)

Recommender Systems Today and Tomorrow

Recommender Systems Today and Tomorrow

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 att...

9 Sep 22min

Recommender Systems Optimization Goals

Recommender Systems Optimization Goals

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 acro...

1 Sep 31min

Recommender Systems Origin Story

Recommender Systems Origin Story

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative...

18 Aug 25min

Social Choice for Fair Recommendations

Social Choice for Fair Recommendations

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems,...

27 Jul 42min

News Recommendations

News Recommendations

News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI, ...

2 Jul 46min

Give Users the Wheel

Give Users the Wheel

What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines ...

23 Jun 35min

AutoLike

AutoLike

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explor...

17 Jun 35min

Student Spotlight: Aaron Payne, Data Analyst

Student Spotlight: Aaron Payne, Data Analyst

Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They...

1 Mai 25min

Populært innen Vitenskap

fastlegen
romkapsel
tingenes-tilstand
jss
liberal-halvtime
forskningno
sinnsyn
rekommandert
villmarksliv
rss-paradigmepodden
fjellsportpodden
tomprat-med-gunnar-tjomlid
grunnstoffene
nordnorsk-historie
rss-nysgjerrige-norge
tidlose-historier
psykopoden
rss-rekommandert
rss-inn-til-kjernen-med-sunniva-rose
smart-forklart