List of all long papers accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
- A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets
Yoshifumi Seki, Takanori Maehara
- A Ranking Optimization Approach to Latent Linear Critiquing in Conversational Recommender System
Hanze Li, Scott Sanner, Kai Luo, Ga Wu
- Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Chang Li, Haoyun Feng, Maarten de Rijke
- Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation
Yin Zhang, Ziwei Zhu, Yun He, James Caverlee
- Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
- Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Xu HE, Bo An, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang
- Debiasing Item-to-Item Recommendations With Small Annotated Datasets
Tobias Schnabel, Paul N. Bennett
- Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
Guy Aridor, Duarte Goncalves, Shan Sikdar
- Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
Yuta Saito
- Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance
Mesut Kaya, Derek Bridge, Nava Tintarev
- Exploiting Performance Estimates for Augmenting Recommendation Ensembles
Gustavo Penha, Rodrygo L. T. Santos
- Exploring Clustering of Bandits for Online Recommendation System
Liu Yang, Bo Liu, Leyu Lin, Feng Xia, Kai Chen, Qiang Yang
- FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation
Jing Lin, Weike Pan, Zhong Ming
- From the Lab to Production: A Case Study of Session-Based Recommendations in the Home-Improvement Domain
Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Xiquan Cui, Edo Liberty, Khalifeh Al Jadda
- Global and Local Differential Privacy for Collaborative Bandits
Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang
- Goal-driven Command Recommendations for Analysts
Samarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin
- ImRec: Learning Reciprocal Preferences Using Images
James Neve, Ryan McConville
- In-Store Augmented Reality-Enabled Product Comparison and Recommendation
Jesús Omar Álvarez Márquez, Jürgen Ziegler
- Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems
Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof
- KRED: Knowledge-Aware Document Representation for News Recommendations
Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie
- Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Xu HE, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang
- Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Darius Afchar, Romain Hennequin
- MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems
Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches
- Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Mawulolo Koku Ameko, Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany Teachman, Laura E Barnes
- On Target Item Sampling in Offline Recommender System Evaluation
Rocío Cañamares, Pablo Castells
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong
- PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang, YAO HU, Alexander Tuzhilin
- Recommendations as Graph Explorations
Marialena Kyriakidi, Georgia Koutrika, Yannis Ioannidis
- Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de
Panagiotis Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, Andreas Unterhuber, Ludovik Coba, Markus Zanker
- RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues
Théo Moins, Daniel Aloise, Simon J. Blanchard
- Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
Jiaxi Tang, Hongyi Wen, Ke Wang
- SSE-PT: Sequential Recommendation Via Personalized Transformer
Liwei Wu, Shuqing Li, Cho-jui Hsieh, James Sharpnack
- TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
Jin Peng Zhou, Zhaoyue Cheng, Felipe Perez, Maksims Volkovs
- Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
Sami Khenissi, Boujelbene Mariem, Olfa Nasraoui
- Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly
- Unbiased Ad Click Prediction for Position-aware Advertising Systems
Bowen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin
- Unbiased Learning for the Causal Effect of Recommendation
Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma
- What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
Gustavo Penha, Claudia Hauff
- Who Doesn’t Like Dinosaurs? Finding and Eliciting Richer Preferences for Recommendation
Tobias Schnabel, Gonzalo Ramos, Saleema Amershi
List of all short papers accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
- Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation
Yagmur Gizem Cinar, Jean-Michel Renders
- ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
Fei Mi, Xiaoyu Lin, Boi Faltings
- Carousel Personalization in Music Streaming Apps with Contextual Bandits
Walid Bendada, Guillaume Salha, Théo Bontempelli
- Causal Inference on Recommender Systems
Yixin Wang, Dawen Liang, Laurent Charlin, David Blei
- ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering
Denis Kotkov, Qian Zhao, Kati Launis, Mats Neovius
- Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
Francisco J. Peña, Diarmuid O’Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor
- Contextual Meta-Bandit for Recommender Systems Selection
Marlesson R. O. de Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Renan M. de Oliveira, Sandor Caetano, Anderson Soares
- Deconfounding User Satisfaction Estimation from Response Rate Bias
Konstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed Chi, Minmin Chen
- Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Dalin Guo, Sofia Ira Ira Ktena, Pranay Kumar Kumar Myana, Ferenc Huszar, Wenzhe Shi, Alykhan Tejani, Michael Kneier, Sourav Das
- Explainable Recommendation for Repeat Consumption
Kosetsu Tsukuda, Masataka Goto
- Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
Oren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein
- Exploring Longitudinal Effects of Session-based Recommendations
Andres Ferraro, Dietmar Jannach, Xavier Serra
- Fit to Run: Personalised Recommendations for Marathon Training
Jakim Berndsen, Barry Smyth, Aonghus Lawlor
- Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
Dmitri Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez
- History-Augmented Collaborative Filtering for Financial Recommendations
Baptiste Barreau, Laurent Carlier
- Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
Chu-Jen Shao, Hao-Ming Fu, Pu-Jen Cheng
- Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games
Andrés Villa, Vladimir Araujo, Francisca Cattan, Denis Parra
- Long-tail Session-based Recommendation
Siyi Liu, Yujia Zheng
- MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation
Sung Min Cho, Eunhyeok Park, Sungjoo Yoo
- Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Suvadip Paul, Deepak Dilipkumar, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, Wenzhe Shi
- Performance of Hyperbolic Geometry Models in Top-N Recommendation Tasks
Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan Oseledets, Alexander Tuzhilin
- Personality Bias of Music Recommendation Algorithms
Alessandro Melchiorre, Eva Zangerle, Markus Schedl
- Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners
Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth
- Reducing Energy Waste in Households Through Real-Time Recommendations
Janhavi Dahihande, Akshay Anil Jaiswal, Akshay Anil Pagar, Ajinkya Thakare, Magdalini Eirinaki, Iraklis Varlamis
- Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning
Ziwei Zhu, Yun He, Yin Zhang, James Caverlee
- Using Conceptual Incongruity as a Basis for Making Recommendations
Tushar Shandhilya, Nisheeth Srivastava
List of all reproducibility papers accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
-
Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison
Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, Cong Geng
-
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang, John Anderson
List of all demos accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
-
A Federated Recommender System for Online Services
Ben Tan, Bo Liu, Vincent Zheng, Qiang Yang
-
AutoRec: An Automated Recommender System
Ting-Hsiang Wang, Xia Hu, Haifeng Jin, Qingquan Song, Xiaotian Han, Zirui Liu
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Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Rohan Anand, Joeran Beel
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BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis, Shangsong Liang, Siwei Liu, Guangtao Zeng, Junhua Liang, Yucheng Liang, Qiang Zhang, Yaxiong Wu
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Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG
Martin Mladenov, Chih-wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
-
Fairness-aware Recommendation with librec-auto
Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury
-
PicTouRe – A Picture-Based Tourism Recommender
Mete Sertkan, Julia Neidhardt, Hannes Werthner
-
Recommender-Systems.com: A Central Platform for the Recommender-System Community
Joeran Beel
-
VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic
Aaron Rodden, Tarun Salh, Eriq Augustine, Lise Getoor
List of all late-breaking results accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
- A College Major Recommendation System
Samuel Stein, Gary Weiss, Yiwen Chen, Daniel Leeds
- A Joint Dynamic Ranking System with DNN and Vector-based Clustering Bandit
Yu Liu, Xiaoxiao Xu, Jincheng Wang, Yong Li, Changping Peng, Yongjun Bao, Weipeng Yan
- Closed-Form Models for Collaborative Filtering with Side-Information
Olivier Jeunen, Jan Van Balen, Bart Goethals
- Context-aware Graph Embedding for Session-based News Recommendation
Heng-Shiou Sheu, Sheng Li
- Do Channels Matter?: Illuminating Interpersonal Influence on Music Recommendations
Hyun Jeong Kim, So Yeon Park, Minju Park, Kyogu Lee
- Don’t Judge a Book by its Cover: Exploring Book Traits Children Favor
Ashlee Milton, Levesson Batista, Garrett Allen, Siqi Gao, Yiu-Kai Ng, Maria Soledad Pera
- DRecPy: A Python Framework for Developing Deep Learning-Based Recommenders
Fábio Colaço, Márcia Barros, Francisco M. Couto
- Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis
- Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions
Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas
- Investigating Listeners’ Responses to Divergent Recommendations
Rishabh Mehrotra, Chirag Shah, Benjamin Carterette
- Investigating the Impact of Audio States in Music Streaming Sessions
Aaron Ng, Rishabh Mehrotra
- Learning Representations of Hierarchical Slates in Collaborative Filtering
Ehtsham Elahi, Ashok Chandrashekar
- Recommending in changing times
Shruti Kunde, Mayank Mishra, Amey Pandit, Shashank Gupta, Rekha Singhal, Manoj Nambiar, Gautam Shroff
- Smart Targeting: A Relevance-driven and Configurable Targeting Framework for Advertising System
Yong Li, Zihao Zhao, Zhiwei Fang, Kui Ma, Yafei Yao, Changping Peng, Yongjun Bao, Weipeng Yan
- The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher
- Towards Multi-Language Recipe Personalisation and Recommendation
Niall Twomey, Andrey Ponikar, Mikhail Fain, Nadine Sarraf
- Tuning Word2vec for Large Scale Recommendation Systems
Ben Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael Bronstein
List of all industry talks accepted for RecSys 2020 (in alphabetical order).
Proceedings are available in the
ACM Digital Library.
- Netflix (Zachary Schendel)
A Human Perspective on Algorithmic Similarity
- IKEA (Balazs Toth)
Balancing relevance and discovery to inspire customers in the IKEA App
- Amazon (Lakshmi Ramachandran)
Behavior-based Popularity Ranking on Amazon Video
- SBX Technologies & Matching Agent (R. Ramanathan)
Building a reciprocal recommendation system at scale from scratch: Learnings from one of Japan’s prominent dating applications
- Huawei (Zhenhua Dong)
Counterfactual learning for recommender system
- Chegg (Deb Sanghamitra)
Developing Recommendation System to provide a Personalized Learning experience at Chegg
- Globo.com (Felipe Ferreira)
Investigating Multimodal Features for Video Recommendations at Globoplay
- Talto (Markus Reiter-Haas)
On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career
- Etsy (Moumita Bhattacharya)
Query as Context for Item-to-Item Recommendation
- Coveo (Jacopo Tagliabue)
The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference
