Academia Keynote Speaker Dr. Dietmar Jannach

Dietmar Jannach is a full professor of Information Systems at AAU Klagenfurt, Austria. Before joining AAU in 2017, he was a professor of Computer Science at TU Dortmund, Germany. In his research, he focuses on the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In the last years, Dietmar Jannach worked on various practical aspects of recommender systems. He is the main author of the first text book on the topic published by Cambridge University Press in 2010 and was the co-founder of a tech startup that created an award-winning product for interactive advisory solutions. 


Industry Keynote Speaker Dr. Linas Baltrunas

Linas Baltrunas is a Senior Researcher/Engineer on the personalization team at Netflix in Los Gatos, California. He received his Ph.D. in Computer Science from the University of Bolzano specializing in Context Aware Recommender Systems. Working with professor Francesco Ricci, he pioneered and popularized work on Context Aware Collaborative Filtering. His work in this area range from early heuristic methods, such as item and user micro-profiling, to various applications of tensor factorization and LSTMs. He continued his work in recommendations focusing on learning to rank at Telefonica Research, Spain. Linas is an active member of RecSys community where he served as an Industry co-chair in 2017 and co-organized the Context Aware Recommender Systems workshop series. His work on Tensor Factorization for Context Aware systems was recognized as one the top 5 most cited papers of RecSys conference. 

Accepted Papers

  1. Deep Context-Aware Recommender System Utilizing Sequential Latent Context, Amit Livne, Moshe Unger, Bracha Shapira and Lior Rokach (Long Paper)
  2. Deep Joint Embeddings of Context and Content for Recommendation, Miklas S. Kristoffersen, Jacob L. Wieland, Sven E. Shepstone, Zheng-Hua Tan and Vinoba Vinayagamoorthy (Short Paper
  3. On-device User Intent Prediction for Context and Sequence Aware Recommendation,  
    Benu Changmai, Divija Nagaraju, Debi Mohanty, Kriti Singh, Kunal Bansal and Sukumar Moharana  (Long Paper)
  4. Repeat Recommendation Considering Contextual Information, Qian Zhang, Masahiro Sato, Sho Takemori, Janmajay Singh, Takashi Sonoda and Tomoko Ohkuma (Short Paper)
  5. Variational Bayesian Context-aware Representation for Grocery Recommendation,  Zaiqiao Meng, Richard McCreadie, Craig Macdonald and Iadh Ounis (Short Paper)