Recommendation and personalization research has expanded far beyond its traditional home in computer science. Today, scholars across disciplines—including information systems, communication science, psychology, marketing, education, media studies, and sociology—investigate how recommendations are designed, perceived, evaluated, regulated, and embedded in organizational and societal contexts.

The BEYOND workshop aims serve as a meeting point for these diverse research traditions. Rather than centering solely on algorithmic innovation, we seek to foreground theoretical, methodological, behavioral, organizational, ethical, and societal perspectives on recommendation. We explicitly welcome contributions from researchers working outside core computing venues, as well as integrative work that bridges computational and non-computational approaches.

Topics of Interest

• Interdisciplinary approaches to RS (e.g., psychology, HCI, design, sociology, cognitive science, science and technology studies, computational social science)
• Theoretical, historical, and critical perspectives
  • Critical reflections on the dominance of machine learning in RS research
  • Historical or critical analyses of the evolution of RS research
  • Alternative theoretical frameworks for conceptualizing the recommendation problem
• Human‑centered design, HCI, and interaction
  • Human-centered recommendation methodologies
  • Human-centered evaluation methodologies
  • Design methodologies such as participatory, speculative, or value-sensitive design
  • User experience, trust, and transparency in RS
  • User agency, control, and feedback in the interaction with RS
• Psychology and cognitive science
  • Application of psychological theories to RS (e.g., decision-making, motivation, affect, personality, autonomy)
  • Cognitive science perspectives on information filtering and discovery
  • Preference formation, change, and calibration; choice architecture and nudging vs. autonomy
• Social, cultural, and organizational dimensions
  • Cultural and sociological dimensions of recommendation
  • Computational social science approaches to diffusion, communities, polarization, and diversity
  • Workplace and organizational RS (e.g., hiring, knowledge discovery, collaboration tools)
• Ethics, law, and governance
  • Ethical tensions, value conflicts, and societal implications of RS
  • Auditing, documentation, and governance frameworks; standards for accountability
• Methods and evaluation (diverse and mixed)
  • Qualitative, ethnographic, and interpretive methods (e.g., interviews, diary studies, fieldwork) for RS development, evaluation, and understanding recommendation needs
  • Mixed‑methods, causal inference, RCTs, A/B testing, and quasi‑experiments
  • Longitudinal studies on long‑term impacts (e.g., well‑being, autonomy, preference development)
  • Measurement and metrics that go beyond accuracy (e.g., empowerment, reflection, fairness, welfare)
• Failures, harms, and safety
  • Case studies of RS failures or unintended consequences in real-world applications
  • Risk assessment, hazard analysis, robustness, and adversarial evaluation
  • Mitigation strategies for manipulation, addiction‑like loops, misinformation, and unfairness
• Domains and high‑stakes contexts
  • RS in sensitive or high-stakes contexts (e.g., education, healthcare, mental health)
  • Public sector and nonprofit deployments; community and civic technology contexts
  • Cultural heritage, arts, and media recommendations
• Reframing goals and outcomes
  • Recommender goals oriented toward well-being, reflection, or empowerment
  • Sociotechnical success criteria that balance individual, community, and societal outcomes

We particularly encourage submissions that challenge established paradigms, highlight methodological diversity, or bring underrepresented perspectives into the conversation.

We explicitly welcome contributions from outside core computing as well as work that bridges computational and non-computational approaches.

We also welcome practice-based research and reflective accounts from industry, public sector, and beyond.

Paper Submission

We solicit position papers, research papers, and case study papers of six to ten (6–10) pages (not including references, acknowledgments, and CEUR’s Declaration on Generative AI). Submissions must use the CEURART single-column template, available for download here (ZIP) or via Overleaf, and be submitted in PDF format.

All submissions should be in English and must be original and not under review at any other conference, workshop, or journal at the time of submission.

Papers must be submitted via EasyChair. Make sure to select the “BEYOND - A Workshop on the Interdisciplinarity of Recommender Systems” track when creating a submission.

Each submission will be reviewed by three members of the Program Committee through a single-anonymized peer review process (author names and affiliations should be included in the manuscript; reviewer identities will be hidden). Submissions will be evaluated based on quality, novelty, clarity, interdisciplinarity, and relevance, with an emphasis on fostering engaging discussions at the workshop; where applicable, additional criteria include methodological rigor, and ethical considerations. Accepted papers will be published in the workshop proceedings after the event (most likely via CEUR-WS). Camera-ready instructions, including CEUR metadata, author rights, and formatting checks, will be provided at notification.

At least one author of each accepted paper is expected to attend the conference in person and present at the workshop. At least one author from each accepted paper must register for and attend the workshop in person to present the paper and address audience questions during the Q&A session. We understand that travel conditions may be uncertain for some participants. In the case that accepted authors are unable to travel to the conference due to demonstrable visa or travel issues, they are allowed to record their presentation as a video.

All participants must adhere to a code of conduct, which will be shared before the workshop.

Also refer to the FAQs for further details and questions.

• Paper submission: July 13, 2026, AoE
• Author notification: August 14, 2026, AoE
• Camera-ready version due: August 28, 2026, AoE
• Workshop: October 2, 2026, morning session (8:30–12:30); in the scope of RecSys 2026

• Christine Bauer (University of Salzburg, Austria)
• Eva Zangerle (University of Innsbruck, Austria)
• Alan Said (University of Gothenburg, Sweden)