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. 

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.

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 dynamic properties in online environments.

CARS 2.0 workshop will be held between September 25th and co-located with RecSys 2020 in Rio de Janeiro, Brazil.