Informatik, TU Wien


9th ACM Conference on Recommender Systems

Recommendation is a particular form of information filtering that exploits past behaviors and user / item similarities to generate a list of information items that is personally tailored to an end-user’s preferences. Special topics of the conference include:

  • Conversational recommender systems
  • Context-aware recommenders
  • Evaluation metrics and studies
  • Field and user studies
  • Group recommenders
  • Innovative/New applications
  • Machine learning for recommendation
  • Mobile and multi-channel recommendations
  • Personalization
  • Preference elicitation
  • Privacy and Security
  • Recommendation algorithms and scalability
  • Social recommenders
  • Semantic technologies for recommendation
  • Targeted advertising
  • Trust and reputation
  • Theoretical foundations
  • User interaction, interfaces and modelling

RecSys as the major international conference brings together the main international research groups, along with many of the world’s leading e-commerce companies. It is a very competitive event with an acceptance rate of 22 %, for long papers and short papers (for poster presentation). In addition to the technical track, RecSys 2015 program features keynote and invited talks, tutorials covering state-of-the-art in this domain, a workshop program, an industrial track and a doctoral symposium.

Invited Talks

  • A (persuasive?) speech on automated persuasion, Oliviero Stock (FBK-IRST, Italy)
  • Recommendations within a Social Network: One Step at a Time, Igor Perisic (LinkedIn, USA)


  • Replicable Evaluation of Recommender Systems, Alan Said (Recorded Future, Sweden), Alejandro Bellogín (Universidad Autónoma de Madrid, Spain)
  • Real-time Recommendation of Streamed Data, Frank Hopfgartner (University of Glasgow, UK), Benjamin Kille (TU Berlin, Germany), Tobias Heintz (plista GmbH, Germany), Roberto Turrin (ContentWise, Italy)
  • Scalable Recommender Systems: Where Machine Learning Meets Search!, Joaquin A. Delgado (Verizon, US), Diana Hu (Verizon, US)
  • Interactive Recommender Systems, Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US), Chris Johnson (Spotify Inc., US)