Tentative Program

The following is our tentative program, subject to change.

Complete proceedings are in ScholarWorks.

Format

Each paper session will follow a similar format, with some differences in timing:

• Each paper will present, followed by brief clarifying questions about it while the next presenter sets up.
• The session moderator will introduce and moderate a panel discussion among the presenters to probe the concepts and interplay of the presented work and positions.

Session 1 (9–10:30 AM)

• Opening Remarks

• Setting the Stage

  1. Towards Minimal Necessary Data: The Case for Analyzing Training Data Requirements of Recommender Algorithms. Martha Larson, Alessandro Zito, Babak Loni, and Paolo Cremonesi.
  2. Quantifying Knowledge Segregation in Society. Abhijnan Chakraborty, Muhammad Ali, Saptarshi Ghosh, Niloy Ganguly, and Krishna P. Gummadi.
  3. Balanced Neighborhoods for Fairness-Aware Collaborative Recommendation. Robin Burke, Nasim Sonboli, Masoud Mansoury, and Aldo Ordonez-Gauger.

Coffee (10:30–11 AM)

Session 2 (11 AM–12:30 PM)

• Measuring Fairness and Tradeoffs

  1. Impact of Task Recommendation Systems in Crowdsourcing Platforms. Kathrin Borchert, Matthias Hirth, Steffen Schnitzer, and Christoph Rensing.
  2. Price of fairness for location based advertising. Christopher Riederer and Augustin Chaintreau.
  3. Fair Sharing for Sharing Economy Platforms. Abhijnan Chakraborty, Joanna Asia Biega, Aniko Hannak, and Krishna P. Gummadi.

• Explanations and Persuasion

  1. Explanation Strategies for Matrix Factorization Recommender Systems. Bashir Rastegarpanah, Mark Crovella, and Krishna Gummadi.
  2. Do News Consumers Want Explanations for Personalized News Rankings?. Maartje Ter Hoeve, Mathieu Heruer, Daan Odijk, Anne Schuth, Martijn Spitters, Ron Mulder, Nick van der Wildt, and Maarten de Rijke.
  3. Presenting Challenging Recommendations: Making Diverse News Acceptable. Nava Tintarev.

Lunch (12:30–2 PM)

Session 3 (2–3:30 PM)

• Keynote Address: Discrimination in Decision Making by Krishna Gummadi.

• Fair Algorithms

  1. Academic performance prediction in a gender-imbalanced environment. Piotr Sapiezynski, Valentin Kassarnig, Christo Wilson, Sune Lehmann, and Alan Mislove.
  2. Consideration on Recommendation Independence for a Find-Good-Items Task. Toshihiro Kamishima and Shotaro Akaho.

Coffee (3:30–4 PM)

Session 4 (4–5:30 PM)

• Structured discussion on open problems and a research agenda.