Workshop on Deep Learning for Recommender Systems

The workshop centers around the use of Deep Learning technology in Recommender Systems and algorithms. DLRS 2017 builds upon the positively received traits of DLRS 2016. DLRS 2017 is a fast paced half-day workshop with a focus on high quality paper presentations and keynote. We welcome original research using deep learning technology for solving recommender systems related problems. Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the recommender systems community, deep learning for recommender systems became widely popular in 2016. We believe that the previous edition of this workshop—DLRS 2016—also took its share to popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities.

  • Balázs Hidasi, Gravity, Hungary
  • Alexandros Karatzoglou, Telefonica, Spain
  • Oren Sar-Shalom, IBM, Isreal
  • Sander Dieleman, DeepMind, UK
  • Domonkos Tikk, Gravity, Hungary
  • Bracha Shapira, Ben Gurion University, Isreal


Sunday, Aug 27, 2017, 14:00-17:30


Room 1

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