CARS: Workshop on Context-Aware Recommender Systems

Contextual information has been widely recognized as an important modeling dimension in various social science and technological disciplines and is becoming more and more important for enhancing recommendation results and retrieval performance. 

There have been several CARS workshops organized in the past, including a successful workshop that was held last year with more than 100 participants, 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 context-aware recommender systems (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.  

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, 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.

This year we wish to emphasize novel applications and usages of context in recommender systems, such as context in group dynamics and group decision making, cognitive biases due to irrelevant contextual conditions in decision making, GUI strategies for increasing context awareness, and utilizing user personality and context interactions in CARS.

Topics of interest

We invite contributions to the workshop about topics related to CARS (but are not limited to):

· Context in generative recommender systems

· Explainable context-aware recommender systems

· Sequence-aware and time-aware recommender systems

· Latent context models for recommender systems

· Mobile recommender systems and wearables

· Privacy modeling in context-aware recommendations

· GUI strategies for increasing context awareness

· User personality and dynamic context interactions

· Context influencing the usage of RSs

· Data sets for context-dependent recommendations

· Algorithms for detecting the relevance of contextual data from multiple types of data 

  (semantic web, graphs) and media (text, images, video, speech)

· Human context recognition for health applications

Submission Types and Guidelines

CARS submissions should be prepared in PDF format according to the new single-column format  (Microsoft Word or Latex formats). If you are using Overleaf, you can use the following code (\documentclass[manuscript]{acmart}). The peer-review process is single-blind and handled electronically through EasyChair. Accepted papers will be included in the workshop proceedings and at least one author of each accepted contribution must attend the workshop. Accepted papers are given an oral or a poster presentation slot at the workshop. 

The ideal length of a paper for the CARS workshop is between 4-8 pages (excluding references). Submitted work should be original. However, technical reports or ArXiv disclosure prior to or simultaneous with the workshop submission is allowed, provided they are not peer-reviewed. The organizers also encourage authors to make their code and datasets publicly available.