Presentation slides of #recsys2017

3 min readSep 5, 2017


We have decided to collect all the slides from the 2017 ACM Recommender Systems conference and make them available in this post for everyone. During the following weeks, we will keep adding links. If you are missing the slides from your talk, please contact @acmrecsys on twitter

Keynotes: The slides for all the keynotes that we have been able to collect are available here. For a full program, check our website

  • Recommender Systems and the New New Economics of Information by George Loewenstein (Carnegie Mellon University, USA) [slides upon request]
  • Improving Higher Education — Learning Analytics & Recommender Systems Research by George Karypis (University of Minnesota, USA)
  • Personalization is a Two-Way Street by Ronny Lempel (Outbrain, Israel) [slides]
  • Memory Networks for Recommendation by Jason Weston (Facebook, USA)

Industry: All the slides for the industry session talks are are available in our website

  • Rethinking Collaborative Filtering: A Practical Perspective on State-Of-The-Art Research Based on Real-World Insights and Challenges, by Noam Koenigstein (Microsoft) [slides]
  • Recommendation Applications and Systems at Electronic Arts, by John Kolen (EA) [slides]
  • Search Ranking And Personalization at AirBnB by Mihajlo Grbovic (AirBnB) [slides]
  • Bootstrapping a Destination Recommender System by Neal Lathia (Skyscanner) [slides]
  • Déjà Vu: The Importance of Time and Causality in Recommender Systems by Justin Basilico (Netflix) and Yves Raimond (Netflix) [slides]
  • Building Recommender Systems for Fashion by Nick Landia (Dressipi)[slides]
  • Boosting Recommender Systems with Deep Learning by João Gomes (Farfetch) [slides]
  • Personalization challenges in e-Learning by Roberto Turrin (CloudAcademy) [slides]
  • Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned, by Benjamin Le (LinkedIn) [slides]
  • Online Learning to Rank for Recommender Systems, by Daan Odijk (Blendle) [slides]
  • Bandit Algorithms for e-Commerce Recommender Systems, by Mikael Hammar (Apptus) [slides]

Tutorials: The slides for all the tutorials are available in our website

  • Privacy Privacy for Recommender Systems by Bart Knijnenburg (Clemson University, USA) and Shlomo Berkovsky (CSIRO, Australia) [slides]
  • New Paths in Music Recommender Systems Research by Markus Schedl (Johannes Kepler University Linz, Austria), Peter Knees (Vienna University of Technology, Austria) and Fabien Gouyon (Pandora Inc., USA) [slides]
  • Deep Learning for Recommender Systems by Alexandros Karatzoglou (Telefonica Research, Spain) and Balázs Hidasi (Gravity R&D, Hungary) [slides]
  • Product Recommendations Enhanced with Reviews by Muthusamy Chelliah (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India) [slides]
  • Open Source Tools for Online Learning Recommenders by Róbert Pálovics (Hungarian Academy of Sciences, Hungary), Domokos Kelen (Hungarian Academy of Sciences, Hungary) and András A. Benczúr (Hungarian Academy of Sciences, Hungary) [slides]

Papers: The slides for all the papers that we have been able to collect are available here. For a full program, check our website

  • Déjà Vu: The Importance of Time and Causality in Recommender Systems by Justin Basilico and Yves Raimond [link]
  • Evaluating decision-aware recommender systems by Rus M. Mesas and Alejandro Bellogin [link]
  • Revisiting neighborhood-based recommenders for temporal scenarios by Alejandro Bellogin and Pablo Sánchez [link]
  • Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System by by Alain Starke, Martijn Willemsen and Chris Snijders [link]
  • Online Learning to Rank by Daan Odijk [link]
  • Understanding How People Use Natural Language to Ask for Recommendations by by Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, and F. Maxwell Harper [link]
  • Defining and Supporting Narrative-driven Recommendation by Toine Bogers and Marijn Koolen [link]
  • Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networksby Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi and Paolo Cremonesi [link]
  • Educational Question Routing in Online Student Communitiesby Jakub Macina, Ivan Srba, Joseph Jay Williams and Maria Bielikova [link]
  • When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendationby Dietmar Jannach and Malte Ludewig [link]
Vancouver, Canada

The 12th ACM Conference on Recommender Systems will take place in Vancouver, Canada, 2nd-7th October 2018 — if you want to submit your work or attend the conference, please check our website for more information.




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