• Long paper: Presentation time is 17 mins, and question time is 3 mins.
  • Short paper: Presentation time is 8mins. After three successive presentations, question time is 6 mins.
  • Time zone: US pacific daylight time (UTC -7)

09:00 - 10:15:Session 1 (on-line)

10:15 - 10:30: Break

10:30 - 11:30: Session 2 (on-line)

  • Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
    • Danny Stax, Manel Slokom, and Martha Larson
    • article
  • Random Isn’t Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
    • Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, and Luca Belli
    • article
  • Towards Fair Conversational Recommender Systems
    • Shuo Lin, Ziwei Zhu, Jianling Wang, and James Caverlee
    • article

11:30 - 11:40: Break

11:40 - 12:30: Session 3 (on-line)

  • Exposure-Aware Recommendation using Contextual Bandits
    • Masoud Mansoury, Bamshad Mobasher, and Herke Van Hoof
    • article
  • Short Presentations
    • The Users Aren’t Alright: Dangerous Mental Illness Behaviors and Recommendations
      • Ashlee Milton and Stevie Chancellor
      • article
    • Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems
      • Ida Larsen-Ledet, Bhaskar Mitra, and Siân Lindley
      • article
    • Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
      • Hal Ashton and Matija Franklin
      • article

12:30 - 14:00: Lunch

14:00 - 15:00: Session 4 (on-line)

  • Fair Matrix Factorisation for Large-Scale Recommender Systems
    • Riku Togashi and Kenshi Abe
    • article
  • Hidden Author Bias in Book Recommendation
    • Savvina Daniil, Mirjam Cuper, Cynthia C.S. Liem, Jacco Van Ossenbruggen, and Laura Hollink
    • article
  • Inclusive Design: Principles of Inclusive Design Ethics for Recommender Systems

15:00 - 15:20: Break

15:20 - 16:30: Session 5 (in-person)

  • Matching Consumer Fairness Objectives & Strategies for RecSys
    • Michael Ekstrand and Maria Soledad Pera
    • article
  • The Role of Bias in News Recommendation in the Perception of the COVID-19 Pandemic
    • Thomas Elmar Kolb, Irina Nalis-Neuner, Mete Sertkan, and Julia Neidhardt
    • article
  • Short Presentations
    • A Stakeholder-Centered View on Fairness in Music Recommender Systems
      • Karlijn Dinnissen and Christine Bauer
      • article
    • Who Pays? Personalization, Bossiness and the Cost of Fairness
      • Farastu Paresha, Nicholas Mattei, and Robin Burke
      • article
    • What Are Filter Bubbles Really? A Review of the Conceptual and Empirical Work
      • Lien Michiels, Jens Leysen, Annelien Smets, and Bart Goethals
      • article

16:30 - 16:40: Break

16:40 - 17:30: Session 6 (in-person)

  • Analyzing the Effect of Sampling in GNNs on Individual Fairness
    • Rebecca Salganik, Fernando Diaz, and Golnoosh Farnadi
    • article
  • Short Presentations
    • Towards Responsible Medical Diagnostics Recommendation Systems
      • Daniel Schlör and Andreas Hotho
      • article
    • Discussion about attacks and defenses for fair and robust recommendation system design
    • RecSys Fairness Metrics: Many to Use But Which One To Choose?
      • Jessie J. Smith, and Lex Beattie
      • article

17:30 - 17:40: Closing Remarks