Accepted Contributions

List of all papers accepted for RecSys 2022 (in alphabetical order).

 

• A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models
  Yingcan Wei (NVIDIA, China), Matthias Langer (NVIDIA, China), Fan Yu (NVIDIA, China), Minseok Lee (NVIDIA, Korea, Republic of), Jie Liu (NVIDIA, China), Ji Shi (NVIDIA, China), Zehuan Wang (NVIDIA, China)
• A User-Centered Investigation of Personal Music Tours
  Giovanni Gabbolini (University College Cork, Ireland), Derek Bridge (University College Cork, Ireland)
• Adversary or Friend? An adversarial Approach to Improving Recommender Systems
  Pannaga Shivaswamy (Netflix Inc, United States), Dario Garcia Garcia (Netflix, United States)
• Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation
  Wei Cai (Zhejiang university, China), Weike Pan (Shenzhen University, China), Jingwen Mao (Computer Science, China), Zhechao Yu (Zhejiang University, China), congfu xu (Zhejiang University, China)
• BRUCE – Bundle Recommendation Using Contextualized item Embeddings
  Tzoof Avny Brosh (Ben Gurion , Israel), Amit Livne (Ben-Gurion University of the Negev, Israel, Ben-Gurion University of the Negev, Israel), Oren Sar Shalom (Facebook, Israel, Facebook, Israel), Bracha Shapira (Ben-Gurion University of the Negev, Israel, Ben-Gurion University of the Negev, Israel), Mark Last (Ben-Gurion University of the Negev, Israel, Ben-Gurion University of the Negev, Israel)
• Bundle MCR: Towards Conversational Bundle Recommendation
  Zhankui He (UC San Diego, United States, UC San Diego, United States), Handong Zhao (Adobe Research, United States, Adobe Research, United States), Tong Yu (Adobe Research, United States, Adobe Research, United States), Sungchul Kim (Adobe Research, United States), Fan Du (Adobe Research, United States, Adobe Research, United States), Julian McAuley (UC San Diego, United States, UC San Diego, United States)
• CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
  Rui Ma (Alibaba Group, China), Ning Liu (Tsinghua University, China), Jingsong Yuan (Alibaba Group, China), Huafeng Yang (Alibaba Group, China), Jiandong Zhang (Alibaba Group, China)
• Context and Attribute-Aware Sequential Recommendation via Cross-Attention
  Ahmed Rashed (University of Hildesheim, Germany), Shereen Elsayed (University of Hildesheim, Germany), Lars Schmidt-Thieme (University of Hildesheim, Germany)
• Countering Popularity Bias by Regularizing Score Differences
  Wondo Rhee (Seoul National University, Korea, Republic of), Sung Min Cho (Seoul National University, Korea, Republic of), Bongwon Suh (Seoul National University, Korea, Republic of)
• Defending Substitution-based Profile Pollution Attacks on Sequential Recommenders
  Zhenrui Yue (University of Illinois Urbana-Champaign, United States), Huimin Zeng (University of Illinois Urbana-Champaign, United States), Ziyi Kou (University of Illinois Urbana-Champaign, United States), Lanyu Shang (University of Illinois Urbana-Champaign, United States), Dong Wang (University of Illinois at Urbana-Champaign, United States)
• Denoising Self-Attentive Sequential Recommendation
  Huiyuan Chen (Visa Research, United States, Visa Research, United States), Yusan Lin (Visa Research, United States, Visa Research, United States), Menghai Pan (Visa Research, United States, Visa Research, United States), Lan Wang (Visa Research, United States), Chin-Chia Michael Yeh (Visa Inc, United States, Visa Inc, United States), Xiaoting Li (Visa Research , United States, Visa Research , United States), Yan Zheng (Visa Research, United States, Visa Research, United States), Fei Wang (Visa Research, United States, Visa Research, United States), Hao Yang (Visa Research, United States, Visa Research, United States)
• Don’t recommend the obvious: estimate probability ratios
  Roberto Pellegrini (Amazon Development Centre Scotland, United Kingdom), Wenjie Zhao (Amazon Development Centre Scotland, United Kingdom), Iain Murray (Amazon Development Centre Scotland, United Kingdom, University of Edinburgh, United Kingdom)
• Dual Attentional Higher Order Factorization Machines
  Arindam Sarkar (Amazon, India), Dipankar Das (Amazon, India), Vivek Sembium (Amazon, India), Prakash Mandayam Comar (Amazon, India)
• Dynamic Global Sensitivity for Differentially Private Contextual Bandits
  Huazheng Wang (Princeton University, United States), David B. Zhao (University of Virginia, United States), Hongning Wang (University of Viriginia, United States)
• EANA: Reducing Privacy Risk on Large-scale Recommendation Models
  Lin Ning (Google Research, United States), Steve Chien (Google Research, United States), Shuang Song (Google Research, United States), Mei Chen (Google, United States), Qiqi Xue (Google, United States), Devora Berlowitz (Google Research, United States)
• Effective and Efficient Training for Sequential Recommendation using Recency Sampling
  Aleksandr Petrov (the University of Glasgow, United Kingdom), Craig Macdonald (University of Glasgow, United Kingdom)
• Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
  Minju Park (Seoul National University, Korea, Republic of), Kyogu Lee (Seoul National University, Korea, Republic of)
• Exploring the longitudinal effect of nudging on users’ genre exploration behavior and listening preference
  Yu Liang (’s-Hertogenbosch, Netherlands), Martijn C. Willemsen (Eindhoven University of Technology, Netherlands, Jheronimus Academy of Data Science, Netherlands)
• Fairness-aware Federated Matrix Factorization
  Shuchang Liu (Rutgers University, United States), Yingqiang Ge (Rutgers University, United States), Shuyuan Xu (Rutgers University, United States), Yongfeng Zhang (Rutgers University, United States), Amelie Marian (Rutgers University, United States)
• Fast And Accurate User Cold-Start Learning Using Monte Carlo Tree Search
  Dilina Chandika Rajapakse (Trinity College Dublin, Ireland), Douglas Leith (Trinity College Dublin, Ireland)
• Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
  Weixin Chen (Shenzhen University, China), Mingkai He (Shenzhen University, China), Yongxin Ni (National University of Singapore, Singapore), Weike Pan (Shenzhen University, China), Li Chen (Hong Kong Baptist University, Hong Kong), Zhong Ming (Shenzhen University, China)
• Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy
  Maryam Aziz (Spotify, United States), Jesse Anderton (Spotify, United States), Kevin Jamieson (University of Washington, United States), Alice Y. Wang (Spotify, United States), Hugues Bouchard (Spotify, United States), Javed Aslam (Northeastern University, United States)
• Learning Recommendations from User Actions in the Item-poor Insurance Domain
  Simone Borg Bruun (University of Copenhagen, Denmark), Maria Maistro (University of Copenhagen, Denmark), Christina Lioma (University of Copenhagen, Denmark)
• Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
  Ori Katz (Microsoft, Israel, Technion, Israel), Oren Barkan (Microsoft, Israel, The Open University, Israel), Noam Koenigstein (Microsoft, Israel, Tel-Aviv University, Israel), Nir Zabari (Microsoft, Israel, The Hebrew University, Israel)
• MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer
  Bhumika . (IIT Jodhpur, India), Debasis Das (Indian Institute of Technology (IIT), India)
• Modeling Two-Way Selection Preference for Person-Job Fit
  Chen Yang (Renmin University of China, China), Yupeng Hou (Gaoling School of Artificial Intelligence, China), Yang Song (BOSS zhipin, China), Tao Zhang (BOSS zhipin, China), Ji-Rong Wen (Gaoling School of Artificial Intelligence, China), Wayne Xin Zhao (Renmin University of China, China)
• Modeling User Repeat Consumption Behavior for Online Novel Recommendation
  Yuncong Li (Tencent, China), Cunxiang Yin (Tencent, China), yancheng he (Tencent, China), Guoqiang Xu (Tencent, China), Jing Cai (tencent, China), leeven luo (technology zone, China), Sheng-hua Zhong (Shenzhen University, China)
• Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation
  Yaxiong Wu (University of Glasgow, United Kingdom), Craig Macdonald (University of Glasgow, United Kingdom), Iadh Ounis (University of Glasgow, United Kingdom)
• Off-Policy Actor Critic for Recommender Systems
  Minmin Chen (Google, United States), Can Xu (Google Inc, United States), Vince Gatto (Google, United States), Devanshu Jain (Google, United States), Aviral Kumar (Google, United States), Ed Chi (Google, United States)
• ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
  Alessandro B. Melchiorre (Johannes Kepler University, Austria, Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria), Navid Rekabsaz (Johannes Kepler University, Austria, Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria), Christian Ganhör (Johannes Kepler University, Austria), Markus Schedl (Johannes Kepler University Linz, Austria, Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria)
• RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
  Sanne Vrijenhoek (Universiteit van Amsterdam, Netherlands), Gabriel Bénédict (University of Amsterdam, Netherlands), Mateo Gutierrez Granada (RTL Nederland B.V., Netherlands), Daan Odijk (RTL Nederland B.V., Netherlands), Maarten de Rijke (University of Amsterdam, Netherlands)
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
  Shijie Geng (Rutgers University, United States), Shuchang Liu (Rutgers University, United States), Zuohui Fu (Rutgers University, United States), Yingqiang Ge (Rutgers University, United States), Yongfeng Zhang (Rutgers University, United States)
• Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation
  Karthik Shivaram (Tulane University, United States), Ping Liu (Illinois Institute of Technology, United States), Matthew Shapiro (Illinois Institute of Technology, United States), Mustafa Bilgic (Illinois Institute of Technology, United States), Aron Culotta (Tulane University, United States)
• Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales
  Shuyang Li (UC San Diego, United States), Bodhisattwa Prasad Majumder (UC San Diego, United States), Julian McAuley (UC San Diego, United States)
• Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
  Kohei Hirata (Osaka University, Japan), Daichi Amagata (Osaka University, Japan), Sumio Fujita (Yahoo Japan Corporation, Japan), Takahiro Hara (Osaka University, Japan)
• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
  Huiyuan Chen (Visa Research, United States, Visa Research, United States), Xiaoting Li (Visa Research , United States, Visa Research , United States), Kaixiong Zhou (Rice University, United States), Xia Hu (Rice University, United States), Chin-Chia Michael Yeh (Visa Inc, United States, Visa Inc, United States), Yan Zheng (Visa Research, United States, Visa Research, United States), Hao Yang (Visa Research, United States, Visa Research, United States)
• Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
  Kiwan Maeng (Meta, United States, Pennsylvania State University, United States), Haiyu Lu (Meta, United States), Luca Melis (Meta, United States), John Nguyen (Meta, United States), Mike Rabbat (Meta, United States), Carole-Jean Wu (Meta, United States)
• Towards Psychologically Grounded Dynamic Preference Models
  Mihaela Curmei (Berkeley, United States), Andreas Haupt (Massachusetts Institute of Technology, United States), Dylan Hadfield-Menell (Massachusetts Institute of Technology, United States), Benjamin Recht (University of California – Berkeley, United States)
• You Say Factorization Machine, I Say Neural Network – It’s All in the Activation
  Chen Almagor (The Hebrew University of Jerusalem, Israel), Yedid Hoshen (The Hebrew University of Jerusalem, Israel)

List of all Reproducibility papers accepted for RecSys 2022 (in alphabetical order).

 

• A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation
  Aleksandr Petrov (the University of Glasgow), Craig Macdonald (University of Glasgow)
• Revisiting the Performance of iALS on Item Recommendation Benchmarks
  Steffen Rendle (Google Research), Walid Krichene (Google), Li Zhang (Google Research), Yehuda Koren (Google)
• Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood
  Sara Latifi (University of Klagenfurt), Dietmar Jannach (University of Klagenfurt)

List of all Late-breaking Results papers accepted for RecSys 2022 (in alphabetical order).

 

• Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization

  Bruno Sguerra (Deezer), Viet-Anh Tran (Deezer), Romain Hennequin (Deezer)

• Do recommender systems make social media more susceptible to misinformation spreaders?

  Antonela Tommasel (ISISTAN, CONICET-UNICEN), Filippo Menczer (Indiana University)

• Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

  Hossein A. Rahmani (University College London), Mohammadmehdi Naghiaei (University of Southern California), Ali Tourani (University of Luxembourg), Yashar Deldjoo (Polytechnic University of Bari)

• Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules

  Giuseppe Spillo (University of Bari), Cataldo Musto (University of Bari), Marco de Gemmis (University of Bari Aldo Moro), Pasquale Lops (University of Bari Aldo Moro), Giovanni Semeraro (University of Bari “Aldo Moro”)

• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

  Walid Shalaby (The Home Depot), Sejoon Oh (Georgia Institute of Technology), Amir Afsharinejad (The Home Depot), Xiquan Cui (The Home Depot), Srijan Kumar (Georgia Institute of Technology)

• Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

  Andres Ferraro (McGill University), Gustavo Ferreira (McGill University), Fernando Diaz (Google), Georgina E. M. Born (University College London)

• Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems

  Alexey Grishanov (Moscow Institute of Physics and Technology, Sber AI Lab), Anastasia Ianina (Moscow Institute of Physics and Technology), Konstantin Vorontsov (Moscow Institute of Physics and Technology)

• Position Awareness Modeling with Knowledge Distillation for CTR Prediction

  Congcong Liu (JD.com), Yuejiang Li (JD.com), Jian Zhu (JD.com), Fei Teng (JD.com), Xiwei Zhao (Jd.com), Changping Peng (JD.com), Zhangang Lin (JD.com), Jingping Shao (JD.com)

• Recommender Systems and Algorithmic Hate

  Jessie Smith (University of Colorado Boulder), Lucia Jayne (University of Colorado Boulder), Robin Burke (University of Colorado, Boulder)

• Scalable Linear Shallow Autoencoder for Collaborative Filtering

  Vojtěch Vančura (Czech Technical University), Rodrigo Alves (Czech Technical University), Petr Kasalicky (Czech Technical University), Pavel Kordík (Czech Technical University)

• The Effect of Feedback Granularity on Recommender Systems Performance

  Ladislav Peska (Charles University), Stepan Balcar (Charles University)

• Towards Recommender Systems with Community Detection and Quantum Computing

  Riccardo Nembrini (Politecnico di Milano), Costantino Carugno (Politecnico di Milano), Maurizio Ferrari Dacrema (Politecnico di Milano), Paolo Cremonesi (Politecnico di Milano)

• Towards the Evaluation of Recommender Systems with Impressions

  Fernando Benjamin Perez Maurera (Politecnico di Milano), Maurizio Ferrari Dacrema (Politecnico di Milano), Paolo Cremonesi (Politecnico di Milano)

List of all Demonstration papers accepted for RecSys 2022 (in alphabetical order).

 

• Building and Deploying a Multi-Stage Recommender System with Merlin

  Karl Higley (NVIDIA), Even Oldridge (NVIDIA), Ronay Ak (NVIDIA), Sara Rabhi (NVIDIA), Gabriel de Souza Pereira Moreira (NVIDIA)

• DAGFiNN: A Conversational Conference Assistant

  Ivica Kostric (University of Stavanger), Krisztian Balog (University of Stavanger), Tølløv Alexander Sveen Aresvik (University of Stavanger), Nolwenn Bernard (University of Stavanger), Eyvinn Thu Dørheim (University of Stavanger), Pholit Hantula (University of Stavanger), Sander Havn-Sørensen (University of Stavanger), Rune Henriksen (University of Stavanger), Hengameh Hosseini (University of Stavanger), Ekaterina Khlybova (University of Stavanger), Weronika Lajewska (University of Stavanger), Sindre Ekrheim Mosand (University of Stavanger), Narmin Orujova (University of Stavanger)

• HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Their Caregivers

  Behnam Rahdari (University of Pittsburgh), Peter Brusilovsky (University of Pittsburgh), Daqing He (University of Pittsburgh), Khushboo Maulikmihir Thaker (University of Pittsburgh), Zhimeng Luo (University of pittsburgh), Young Ji Lee (University of Pittsburgh)

• RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data

  Lien Michiels (Froomle), Robin Verachtert (Froomle), Bart Goethals (University of Antwerp)

• RepSys: Framework for Interactive Evaluation of Recommender Systems

  Jan Šafařík (Czech Technical University), Vojtěch Vančura (Czech Technical University), Pavel Kordík (Czech Technical University)

• Who do you think I am? Interactive User Modelling with Item Metadata

  Joey De Pauw (University of Antwerp), Koen Ruymbeek (University of Antwerp), Bart Goethals (University of Antwerp)

List of all doctoral symposium papers accepted for RecSys 2022 (in alphabetical order).

 

• An Interpretable Neural Network Model for Bundle Recommendations
  Xinyi Li (Northwestern University), Edward C. Malthouse (Northwestern University)
• Designing and evaluating explainable AI for non-AI experts: challenges and opportunities
  Maxwell Szymanski (KU Leuven), Katrien Verbert (KU Leuven), Vero Vanden Abeele (KU Leuven)
• Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems
  Jessie J. Smith (University of Colorado Boulder)
• Enhancing Counterfactual Evaluation and Learning for Recommendation Systems
  Nicolò Felicioni (Politecnico di Milano)
• Fair Ranking Metrics
  Amifa Raj (Boise State University)
• Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation
  Tendai Mukande (Dublin City University,SFI Centre for Research Training in Machine Learning)
• KA-Recsys: Knowledge Appropriate Patient Focused Recommendation Technologies
  Khushboo Thaker (University of Pittsburgh)
• Long-term fairness for Group Recommender Systems with Large Groups
  Patrik Dokoupil (Charles University)
• Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems
  Vincenzo Paparella (Politecnico di Bari)

List of all industry talks and posters accepted for RecSys 2022 (in alphabetical order).

 

Talks

• A Lightweight Transformer for Next-Item Product Recommendation
  Jeffrey Mei (Wayfair LLC), Cole Zuber (Wayfair LLC), Yasaman Khazaeni (Wayfair LLC)
• An Incremental Learning framework for large-scale CTR prediction
  Petros Katsileros (Deeplab), Nikiforos Mandilaras (Deeplab), Dimitrios Mallis (Deeplab), Vassilis Pitsikalis (Deeplab), Stavros Theodorakis (DeepLab), Gil Chamiel (Taboola)
• Augmenting Netflix Search with In-Session Adapted Recommendations
  Moumita Bhattacharya (Netflix Research), Sudarshan Dnyaneshwar Lamkhede (Netflix)
• Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems
  Lex Beattie (Spotify), Henriette Cramer (Spotify), Dan Taber (Spotify)
• Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settings
  moran haham (Outbrain)
• Imbalanced Data Sparsity as Source of Unfair Bias in Collaborative Filtering
  Aditya Joshi (SEEK), CHIN LIN WONG (SEEK), Diego Marinho de Oliveira (Seek), FARHAD ZAFARI (Seek), Fernando Mourão (SEEK), SABIR RIBAS (Seek), Saumya Pandey (SEEK)
• Learning Users’ Preferred Visual Styles in an Image Marketplace
  Raul Gomez Bruballa (Shutterstock), Lauren Burnham-King (Shutterstock), Alessandra Sala (Shutterstock)
• Optimizing product recommendations for millions of merchants
  Kim Peiter Jorgensen (Shopify), Chen Karako (Shopify)
• Personalizing Benefits Allocation Without Spending Money
  Dmitri Goldenberg (Booking.com)
• Recommending for a Multi-Sided Marketplace with Heterogeneous Contents
  Yuyan Wang (Google Research, Brain Team), Long Tao (Uber Tech. Inc), Xian Xing Zhang (Pinterest)
• Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
  Jiajing Xu (Pinterest)
• Reusable Self-Attention Recommender Systems in Fashion Industry Applications
  Marjan Celikik (Zalando SE), Ana Peleteiro Ramallo (Zalando SE), Jacek Wasilewski (Zalando SE)
• Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant Content
  Shayak Banerjee (Peloton Interactive, Inc.), Vijay Pappu (Peloton Interactive, Inc.), Nilothpal Talukder (Peloton Interactive, Inc.), Shoya Yoshida (Peloton Interactive, Inc.), Arnab Bhadury (Peloton Interactive, Inc.), Allison Schloss (Peloton Interactive, Inc.), Jasmine Paulino (Peloton Interactive, Inc.)
• TorchRec: a PyTorch domain library for recommendation systems
  Dmytro Ivchenko (Meta AI), Dennis Van Der Staay (Meta AI), Colin Taylor (Meta AI), Xing Liu (Meta AI), Will Feng (Meta AI), Rahul Kindi (Meta AI), Anirudh Sudarshan (Meta AI), Shahin Sefati (Meta AI)
• Translating the Public Service Media Remit into Metrics and Algorithms
  Andreas Grün (ZDF), Xenija Neufeld (Accso – Accelerated Solutions GmbH)

Posters

• A Multi-Stakeholder Recommender System for Rewards Recommendations
  Luiz Pizzato (Commonwealth Bank of Australia), Naime Ranjbar Kermany (Commonwealth Bank of Australia), Thireindar Min (Commonwealth Bank of Australia), Callum Scott (Commonwealth Bank of Australia), Anna Leontjeva (Commonwealth Bank of Australia)
• Automate Page Template Optimization: An Offline Deep Q-Learning Approach
  Zhou Qin (Amazon) and Wenyang Liu (Amazon)
• Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders
  Dirk Sierag (Stitch Fix) and Kevin Zielnicki (Stitch Fix)
• Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations
  blaz skrlj (Outbrain inc.), Adi Schwartz (Outbrain Inc.), Jure Ferlež (Outbrain inc.), Naama Ziporin (Outbrain inc.)
• Estimating Long-term Effects from Experimental Data
  Ziyang Tang (Amazon), Yiheng Duan (Amazon), Steven Zhu (Gopuff), Stephanie Zhang (Amazon), Lihong Li (Amazon)
• Evolution of Walmart.com Sponsored Products – An ML System Overview
  Stephen Guo (Walmart Global Tech) and Musen Wen (Walmart Global Tech)
• Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
  Jan Hartman (Zemanta, an Outbrain company) and Davorin Kopič (Zemanta, an Outbrain company)
• Flow Moods: Recommending Music by Moods on Deezer
  Théo Bontempelli (Deezer), Benjamin Chapus (Deezer), François Rigaud (Deezer), Mathieu Morlon (Deezer), Marin Lorant (Deezer), Guillaume Salha-Galvan (Deezer)
• Matching Theory-based Recommendation in Online Dating
  Yoji Tomita (CyberAgent, Inc.), Riku Togashi (CyberAgent, Inc.), Daisuke Moriwaki (CyberAgent, Inc.)
• Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference
  Zehuan Wang (NVIDIA), Yingcan Wei (NVIDIA), Minseok Lee (NVIDIA), Matthias Langer (NVIDIA), Fan Yu (NVIDIA), Jie Liu (NVIDIA), Shijie Liu (NVIDIA), Daniel G. Abel (NVIDIA), Xu Guo (NVIDIA), Jianbing Dong (NVIDIA), Ji Shi (NVIDIA), kunlun li (NVIDIA)
• Query Attribute Recommendation at Amazon Search
  Chen Luo (Amazon Search), William Headean (Amazon), Neela Avudaiappan (Amazon), Haoming Jiang (Amazon), Tianyu Cao (Amazon), Qingyu Yin (Amazon), Yifan Gao (Amazon), Zheng Li (Amazon), Rahul Goutam (Amazon), Haiyang Zhang (Amazon), Bing Yin (Amazon)
• Recommendation Systems for Ad Creation: A View from the Trenches
  Manisha Verma (Amazon) and Shaunak Mishra (Amazon)
• Recommendations: They’re in fashion
  Carlos Carvalheira (Farfetch), Tiago Lacerda (Farfetch), Diogo Gonçalves (Farfetch)
• Taxonomic Recommendations of Real Estate Properties with Textual Attribute Information
  Zachary Harrison (Zillow Group) and Anish Khazane (Zillow Group)
• Two-Layer Bandit Optimization for Recommendations
  Siyong Ma (Apple), Puja Das (Apple), Sofia Maria Nikolakaki (Apple), AARON CHEN (Apple), HUMEYRA TOPCU ALTINTAS (Apple)
• Zillow: Volume Governing for Email and Push Messages
  Eric Paul Nichols (Zillow Group), Ruomeng Xu (Zillow Group), Balasubramanian Thiagarajan (Zillow Group), Shruti Kamath (Zillow Group)