| Vol-2431 urn:nbn:de:0074-2431-0 Copyright © 2019 for the individual papers by the papers' authors. Copyright © 2019 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# ACM RecSys LBR 2019
ACM RecSys 2019 Late-breaking Results

### Proceedings of ACM RecSys 2019 Late-breaking Results
co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019)

### Copenhagen, Denmark, September 16-20, 2019.

Edited by

### Marko Tkalcic *
Sole Pera **

* Free University of Bozen-Bolzano, Faculty of Computer Science, Bolzano, Italy
** Boise State University, Boise, Idaho, US

## 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

2019-08-26: submitted by Marko Tkalcic, metadata incl. bibliographic data published under Creative Commons CC0
2019-08-26: published on CEUR-WS.org |valid HTML5|