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, inferring implicit contexts in real-time (online) environments and measuring business metrics for multiple 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 the CARS workshop is to revive the CARS topic and broadly discuss the main features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their properties in online environments.

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


14:0018:00, Attend in Whova

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