ACM RecSys LBR 2019
ACM RecSys 2019 Late-breaking Results

Table of Contents

• Preface
  
  

• A Common Approach for Consumer and Provider Fairness in Recommendations 1-5
  Dimitris Sacharidis , Kyriakos Mouratidis , Dimitrios Kleftogiannis
• BERT, ELMo, USE and InferSent Sentence Encoders: The Panacea for Research-Paper Recommendation? 6-10
  Hebatallah A. Mohamed Hassan , Giuseppe Sansonetti , Fabio Gasparetti , Alessandro Micarelli , Joeran Beel
• Combining Context Features in Sequence-Aware Recommender Systems 11-15
  Sarai Mizrachi , Pavel Levin
• Context-Regularized Neural Collaborative Filtering for Game App Recommendation 16-20
  Shonosuke Harada , Kazuki Taniguchi , Makoto Yamada , Hisashi Kashima
• Data Masking for Recommender Systems: Prediction Performance and Rating Hiding 21-25
  Manel Slokom , Martha Larson , Alan Hanjalic
• Data Pruning in Recommender Systems Research: Best-Practice or Malpractice? 26-30
  Joeran Beel , Victor Brunel
• How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation 31-35
  Linus W. Dietz , Wolfgang Wörndl
• Latent Modeling of Unexpectedness for Recommendations 36-40
  Pan Li , Alexander Tuzhilin
• Negative-Aware Collaborative Filtering 41-45
  Sheng-Chieh Lin , Yu-Neng Chuang , Sheng-Fang Yang , Ming-Feng Tsai , Chuan-Ju Wang
• PQ-VAE: Efficient Recommendation Using Quantized Embeddings 46-50
  Jan Van Balen , Mark Levy
• Towards a Taxonomy of User Feedback Intents for Conversational Recommendations 51-55
  Wanling Cai , Li Chen
• Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation 56-60
  Kyung-Min Kim , Donghyun Kwak , Hanock Kwak , Young-Jin Park , Sangkwon Sim , Jae-Han Cho , Minkyu Kim , Jihun Kwon , Nako Sung , Jung-Woo Ha
• With a Little Help from my Friends: Use of Recommendations at School 61-65
  Maria Soledad Pera , Emiliana Murgia , Monica Landoni , Theo Huibers