Target group:

Industry, Researchers

Abstract:

Recommender systems are tools to support users in finding information and guidance in large information spaces. Famous examples of recommender systems are the ones integrated into Netflix (for recommending movies) and Spotify (for recommending movies). In recent years, recommender systems have become an important technology for both industry and academia. In the Social Computing research area of Know-Center, we aim to bridge both sides by providing our in-house scalable recommender system ScaR, and by constantly improving it with the newest research insights. Therefore, in this edition of Know-Center’s summer academy, we do not only want to give you a general overview of recommender systems and our ScaR framework but also about current research problems in the area of recommender systems such as the identification and mitigation of biases, fairness, and data sparsity issues. Apart from that, we would also like to hear about your individual industry and research-related problems and to discuss how recommender systems could be used to address them.

After the event you will know:

• What a recommender system is and what benefits it can offer


• What Know-Center’s ScaR framework can offer and how it was used in a specific industry use case


• Novel research topics in the area of recommender systems
  
  
  
  • Biases and fairness in recommender systems
  
  
  • Deep learning for session-based recommender systems
  
  
  • Node embeddings and trust in recommender systems
  
  
  
  


• How recommender systems could be used to address your actual problems