Workshop on Context-Aware Recommender Systems

Contextual information has been widely recognized as an important modeling dimension in social science and technological disciplines, and it is becoming more and more important for enhancing the relevancy of the recommendation results. Several context-aware recommender systems (CARS) workshops have been organized in the past, where the addition of contextual information to traditional recommender systems has been discussed. While a substantial amount of research has already been performed, many existing approaches to CARS focus on the so-called `representational view’ that incorporates pre-defined and static contextual factors (such as time and location) in the recommendation process. However, in the past few years, 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 rethink the CARS topic and broadly discuss important 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 dynamic properties. In this respect, the main challenge of the next generation of CARS is to introduce more explainable, flexible, and comprehensive approaches to modeling and using contextual information. We also aim at discussing novel perspectives on how recommender systems can deal with the specific contextual situations that characterise the usage of RSs and bring together researchers with wide-ranging backgrounds to identify important research questions in that field, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of the next generation of context-aware recommender systems.

  • 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, Tel-Aviv University, Israel


Half day.

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