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SUMMARY;CHARSET=UTF-8:Summer Academy Session: Recommender Systems
URL:https://www.sfg.at/e/summer-academy-session-recommender-systems/
DESCRIPTION;CHARSET=UTF-8:Recommender Systems\nRecommender systems are tool
 s to support users in finding information and guidance in large informatio
 n spaces. Famous examples of recommender systems are the ones integrated i
 n Netflix (for recommending movies) and Spotify (for recommending movies).
  In recent years\, recommender systems have become an important technology
  for both industry and academia.\n\nRecommender Systems\n\n16.09.2020 15:0
 0 &#8211\; 17:00 | Language: English | Register free of charge for session
 \n\n\nTarget group:\nIndustry\, Researchers\nAbstract:\nRecommender system
 s are tools to support users in finding information and guidance in large 
 information spaces. Famous examples of recommender systems are the ones in
 tegrated in Netflix (for recommending movies) and Spotify (for recommendin
 g movies). In recent years\, recommender systems have become an important 
 technology for both industry and academia. In the Social Computing researc
 h area of the Know-Center\, we aim to bridge both sides by providing our i
 n-house scalable recommender system ScaR and by constantly improving it wi
 th newest research insights. Therefore\, in this edition of the Know-Cente
 r’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 resea
 rch problems in the area of recommender systems such as the identification
  and mitigation of biases\,fairness and data sparsity issues. Apart from t
 hat\, we would also like to hear about your individual industry- and resea
 rch-related problems and to discuss how recommender systems could be used 
 to address them.\nAfter the event you will know:\n\n\n\nWhat a recommender
  system is and what benefits it can offer\n\n\n\n\n\n\nWhat Know-Center’
 s ScaR framework can offer and how it was used in a specific industry use 
 case\n\n\n\n\n\n\nNovel research topics in the area of recommender systems
 \n\n\n\nBiases and fairness in recommender systems\n\n\n\n\n\n\nDeep learn
 ing for session-based recommender systems\n\n\n\n\n\n\nNode embeddings and
  trust in recommender systems\n\n\n\n\n\n\n\n\n\n\nHow recommender systems
  could be used to address your actual problems\n\n\n\n\nSpeaker\n\n\n\n\n\
 nElisabeth Lex\nResearch Area Manager Social Computing\n\n\n\n\n\n\n\nDomi
 nik Kowald\nDeputy Research Area Manager &#8211\; Social Computing\n\n\n\n
 \n\n\n\nEmanuel Lacić\nTechnical Lead – Social Computing\n\n\n\n\n\n\n\
 nTomislav Đuričić\nResearcher – Social Computing\n\n\n\n\n
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