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.
However, in the past few years, various new context-aware recommender systems (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 reimagine 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 group recommendations and in online environments. 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.
Copyright © 2021 CARS Workshop - All Rights Reserved