Program

Program Brochure and Accepted Contributions

• Official program brochure (as PDF, 6.8MB)
• List of accepted contributions

Conference Schedule

Mon, Oct 6

• Tutorials, sponsored by IBM:
  • 09.00 — 10.30: The Recommender Problem Revisited (T1)
  • 11.00 — 12.30: Personalized Location Recom. on Location-based Social Networks (T2)
  • 14.00 — 15.30: Cross-Domain Recommender Systems (T3)
  • 16.00 — 17.30: Social Recommender Systems (T4)
• Workshops, sponsored by Comcast:
  • W1: Controlled Experimentation in Recom., Ranking & Response Prediction (halfday, a.m.)
  • W2: Recommender Systems and the Social Web (half day, a.m.)
  • W3: Crowdsourcing and Human Computation for Recommender Systems (halfway, p.m.)
  • W4: Interfaces and Human Decision Making for Recommender Systems (full day)
  • W5: New Trends in Content-based Recommender Systems (full day)

Tue, Oct 7

• 08.30 — 09.15: Opening Remarks
• 09.15 — 10.30: Quantifying the Value of Better Recommendations (Keynote by Neil Hunt), sponsored by Google
• 10.30 — 11.00: Break
• 11.00 — 12.30: Session 1: Novel Applications
• 12.30 — 14.00: Lunch Break
• 14.00 — 15.45: Session 2: Novel Setups
• 15.45 — 16.15: Break
• 16.15 — 18.00: Session 3: Cold Start and Hybrid Recommenders
• 18.00 — 18.15: Academia-Industry Speed Dating
• 18.30: Buses depart for Computer History Museum
• 19.00 — 22.00: Reception at Computer History Museum, sponsored by Netflix

Wed, Oct 8

• 08.30 — 10.00: Session 4: Metrics and Evaluation
• 10.00 — 10.30: Break
• 10.30 — 12.00: Session 5: Diversity, Novelty and Serendipity
• 12.00 — 13.30: Lunch Break
• 13.30 — 14.45: Large Scale Machine Learning for Predictive Tasks (Keynote by Jeff Dean), sponsored by Yahoo!
• 14.45 — 16.00: Industry Session I: Mainstream
• 16.00 — 16.30: Break
• 16.30 — 18.00: Session 6: Recommendation Methods and Theory
• 19.00 — 22.00: Banquet and Poster/Demo Session, sponsored by LinkedIn

Thu, Oct 9

• 08.30 — 10.00: Session 7: Ranking and Top-N Recommendation
• 10.00 — 10.30: Break
• 10.30 — 12.00: Industry Session II: Offbeat
• 12.00 — 13.30: Lunch Break
• 13.30 — 14.45: Thoughts on the Future of Recommender Systems (Keynote by Hector Garcia-Molina), sponsored by Pandora
• 14.45 — 15.45: Industry Session III: Panel Discussion, sponsored by Baidu
• 15.45 — 16.15: Break
• 16.15 — 18.00: Session 8: Matrix Factorization
• 18.00 — 18.30: Closing and Sneak Peak at RecSys 2015

Fri, Oct 10

• Industry Excursion,
• Doctoral Symposium, sponsored by NSF
• Workshops, sponsored by Facebook:
  • W6: RecSys Challenge (half day, a.m.)
  • W7: Recommender Systems Evaluation (half day, p.m.)
  • W8: Recommendation Systems for Television and Movies (full day)
  • W9: Large Scale Recommender Systems (full day)