WORKSHOP GOALS

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including ecommerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, the vast majority of existing approaches focuses on recommending the most relevant items to users and does not take into account any additional contextual information, such as time, location, weather, or the company of other people. Therefore, this workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems (CARS). In particular, the workshop covers the following topics:

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Context modeling techniques for recommender systems;

• Context-aware user modeling for recommender systems;

• Data sets for context-dependent recommendations;

• Algorithms for detecting the relevance of contextual data;

• Algorithms for incorporating contextual information into recommendation process;

• Algorithms for building explicit dependencies between contextual features and ratings;

• Interacting with context-aware recommender systems;

• Novel applications for context-aware recommender systems;

• Mobile context-aware recommender systems;

• Context-aware group recommendations;

• Large-scale context-aware recommender systems;

• Evaluation of context-aware recommender systems.

Additional information about the workshop and its program can be found on the workshop website. 1

WORKSHOP FORMAT

CARS will be organized as a full day event, in which authors of selected papers will present their work to the workshop audience.

Depending on the number of participants, interactive elements such as plenary discussions are planned.