Christine Bauer

Christine Bauer

Assistant Professor in Human-Centered Computing

Utrecht University


Christine Bauer is an assistant professor at the Human-Centred Computing group at the Department of Information and Computing Sciences at Utrecht University.

Her research activities center on interactive intelligent systems where she integrates research on intelligent technologies, the interaction of humans with the intelligent system, as well as their interplay. Thereby, she takes a human-centered computing approach, where technology follows humans’ and society’s needs. Central themes in her research are context and context-adaptivity. Recently, she focuses on context-aware recommender systems and concentrates on music recommender systems in particular. Her knowledge and experience with music and the music business are her particular asset for this research. A core interest in her research activities is multi-method evaluations. Further interests span manifold fields such as online self-disclosure and privacy, methods for designing context-adaptive systems, and the creative industries, in particular the music sector.

  • Intelligent interactive systems
  • Context-adaptive systems
  • Recommender systems
  • Human-centered computing
  • Fairness
  • Multi-method evaluation
  • Doctoral degree in Social and Economic Sciences (Business Informatics), 2009

    University of Vienna, Austria

  • MSc in Business Informatics, 2011

    TU Wien, Austria

  • Diploma (equivalent to Master degree) in International Business Administration, 2002

    University of Vienna, Austria

  • Study of Jazz Saxophone

    Konservatorium der Stadt Wien, Austria


Central theme

Interactive intelligent systems
at the intersection of human-centered computing, data science, and artificial intelligence


24 different courses across 14 institutions
RecSys Summer School, Italian National PhD Program of AI
Tutorials at UMAP and ISMIR
Supervision of >60 theses

Community service

PC Co-Chair (e.g., RecSys, UMAP, CHI)
Meta-Reviewer (e.g., CHI, ISMIR, UMAP)
Reviewer for >20 journals and >100 conferences

International experience

Research experience in 4 countries (AT, DE, NL, US)
Teaching experience in 6 countries (AT, DE, IT, NL, SE, US)


Repeatedly speaker and panelist at non-scientific events (e.g., Ars Electronica Festival, Dutch Media Week, VUT Indie Days)
Substantial media coverage (e.g., Financial Times, El País)
Radio interviews (e.g., SWR 2, Ö1, FM4, NPO Radio 1)


WiMIR Mentoring since 2018
Queen Mary University of London since 2021
Allyship Co-Chair at CHI 2022 and CHI 2023

Selected Projects

Beyond clicks! Value-driven recommendations

Beyond clicks! Value-driven recommendations

Many news platforms provides uses with recommendations for a personalized experience that fits their interests and needs. However, aside from the consumption of news articles and user loyalty, additional values are relevant that need to be integrated in news recommender systems.

SpART: Spotlight on Artists in Recommenders Systems

SpART: Spotlight on Artists in Recommenders Systems

The purpose of this project is to understand how current systems used to recommend music affect artists and to propose alternative solutions that could benefit the artists. The goal is to identify potential problems that affect artists, and to draw on the artists’ perspective about how future recommender systems could work.

Multi-method evaluation

Multi-method evaluation

The comprehensive evaluation of the performance of a system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. A single evaluation method or measure is not able to evaluate all relevant aspects in a complex setting where a multitude of stakeholders are involved. Employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome.

Fine-grained culture-aware music recommender systems

Fine-grained culture-aware music recommender systems

This FWF-funded project (2017-2020) aimed to design and implement music recommender systems that are able to meet those requirements by considering different granularity levels of cultural aspects in a comprehensive way.