Recommender systems (RecSys) are being used to suggest products to customers, provide personalized product
information, or even to provide products' reviews. These systems recommend items among a huge number of
possibilities and according to users' interests. Recommender Systems have also been proposed to support the
information selection and decision making processes on ecommerce
web sites. The goal of this new special
track is to provide a forum for researchers and practitioners to share their efforts in addressing current issues,
challenges, novel approaches, and applications within the broad scope of recommender systems.
Papers and contributions are encouraged for any work relating to Recommender Systems. Topics of interest
may include (but are in no way limited to):
o Recommendation Algorithms
o Machine Learning for Recommendation
o MultiAgent
Recommender Systems
o Group Recommendations
o Recommendations and Social Networks, Social Recommenders
o Contextaware
recommenders
o Preference elicitation
o Personalization
o Trust and Recommendations
o Privacy and Security
o Robustness
o Evaluation metrics and studies
o Novel paradigms (Affective computing, sentiment analysis, ...)
o User modelling
o Recommender system user interfaces
o User studies, online user experiments
o Case studies of real world implementations