Workshop on Context-Aware Recommender Systems

Contextual information has been widely recognized as an important modeling dimension in various social science and technological disciplines. While a substantial amount of research
has already been performed in the area of context-aware recommender systems (CARS), many existing approaches focus on the representational view that incorporates pre-defined and static contextual factors (such as time and location) to the recommendation process. There have been several CARS workshops organized in the past, where the addition of contextual information to traditional recommender systems has been discussed. However, in the past few years, various new CARS techniques have been introduced, such as sequence-aware recommender systems and latent context-aware recommender systems. Moreover, new application areas, such as, education, health, cooperative work and affective computing, require the modeling of complex, partially observable and dynamic contextual factors. Hence, the primary goal of CARS 2.0 workshop will be to revive the CARS workshops and discuss the next generation of CARS and application domains that may require a variety of dimensions of contexts and cope with its dynamic properties. In this respect, the main challenge of the next generation of CARS is to introduce more flexible and exhaustive approaches to modeling and using contextual information.

  • Gediminas Adomavicius, University of Minnesota, USA
  • Konstantin Bauman, Temple University, USA
  • Bamshad Mobasher, DePaul University, USA
  • Francesco Ricci, Free University of Bozen-Bolzano, Italy
  • Alexander Tuzhilin, NYU Stern School of Business, USA
  • Moshe Unger, NYU Stern School of Business, USA


Friday, Sept 20, 2019, 14:00-17:30


Room 202+203

Platinum Supporters
Diamond Supporters
Gold Supporters
Silver Supporters
Special Supporter