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ORCIDRecSys 2022: Seattle, WA, USA
- Jennifer Golbeck, F. Maxwell Harper, Vanessa Murdock, Michael D. Ekstrand, Bracha Shapira, Justin Basilico, Keld T. Lundgaard, Even Oldridge:
RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022. ACM 2022, ISBN 978-1-4503-9278-5
Keynotes
- Mor Naaman:
"My AI must have been broken": How AI Stands to Reshape Human Communication. 1 - Catherine D'Ignazio:
Co-designing ML Models with Data Activists. 2
User Modeling
- Yu Liang
, Martijn C. Willemsen:
Exploring the longitudinal effects of nudging on users' music genre exploration behavior and listening preferences. 3-13 - Yuncong Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Jing Cai, Leeven Luo, Sheng-hua Zhong:
Modeling User Repeat Consumption Behavior for Online Novel Recommendation. 14-24 - Giovanni Gabbolini, Derek G. Bridge:
A User-Centered Investigation of Personal Music Tours. 25-34 - Mihaela Curmei, Andreas A. Haupt, Benjamin Recht, Dylan Hadfield-Menell:
Towards Psychologically-Grounded Dynamic Preference Models. 35-48
Sequential Recommendation
- Wei Cai, Weike Pan, Jingwen Mao, Zhechao Yu, Congfu Xu:
Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation. 49-58 - Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang:
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders. 59-70 - Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme:
Context and Attribute-Aware Sequential Recommendation via Cross-Attention. 71-80 - Aleksandr V. Petrov, Craig Macdonald:
Effective and Efficient Training for Sequential Recommendation using Recency Sampling. 81-91 - Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, Hao Yang:
Denoising Self-Attentive Sequential Recommendation. 92-101
Domain-Specific Recommendation
- Chen Yang, Yupeng Hou, Yang Song, Tao Zhang, Ji-Rong Wen, Wayne Xin Zhao:
Modeling Two-Way Selection Preference for Person-Job Fit. 102-112 - Simone Borg Bruun, Maria Maistro, Christina Lioma:
Learning Recommendations from User Actions in the Item-poor Insurance Domain. 113-123 - Yaxiong Wu, Craig Macdonald, Iadh Ounis:
Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation. 124-133 - Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed A. Aslam:
Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy. 134-144
Fairness and Privacy
- Wondo Rhee, Sung Min Cho, Bongwon Suh:
Countering Popularity Bias by Regularizing Score Differences. 145-155 - Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu:
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. 156-167 - Shuchang Liu, Yingqiang Ge, Shuyuan Xu, Yongfeng Zhang, Amélie Marian:
Fairness-aware Federated Matrix Factorization. 168-178 - Huazheng Wang, David Zhao, Hongning Wang:
Dynamic Global Sensitivity for Differentially Private Contextual Bandits. 179-187
Diversity and Novelty
- Roberto Pellegrini, Wenjie Zhao, Iain Murray:
Don't recommend the obvious: estimate probability ratios. 188-197 - Kohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara:
Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively. 198-207 - Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, Maarten de Rijke:
RADio - Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations. 208-219 - Karthik Shivaram, Ping Liu, Matthew A. Shapiro, Mustafa Bilgic, Aron Culotta:
Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation. 220-228 - Minju Park, Kyogu Lee:
Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning. 229-236
Models and Learning I
- Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, Mark Last:
BRUCE: Bundle Recommendation Using Contextualized item Embeddings. 237-245 - Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, Markus Schedl:
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. 246-256 - Huiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang:
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems. 257-267 - Weixin Chen, Mingkai He, Yongxin Ni, Weike Pan, Li Chen, Zhong Ming:
Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions. 268-277 - Rui Ma, Ning Liu, Jingsong Yuan, Huafeng Yang, Jiandong Zhang:
CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment. 278-287 - Zhankui He, Handong Zhao, Tong Yu, Sungchul Kim, Fan Du, Julian J. McAuley:
Bundle MCR: Towards Conversational Bundle Recommendation. 288-298 - Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang:
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). 299-315
Sessions and Interaction
- Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari:
Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation. 316-326 - Shuyang Li, Bodhisattwa Prasad Majumder, Julian J. McAuley:
Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales. 327-337 - Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi:
Off-Policy Actor-critic for Recommender Systems. 338-349
Models and Learning II
- Dilina Chandika Rajapakse, Douglas J. Leith:
Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search. 350-359 - Bhumika, Debasis Das:
MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer. 360-368 - Pannaga Shivaswamy, Dario García-García:
Adversary or Friend? An adversarial Approach to Improving Recommender Systems. 369-377 - Arindam Sarkar, Dipankar Das, Vivek Sembium, Prakash Mandayam Comar:
Dual Attentional Higher Order Factorization Machines. 378-388 - Chen Almagor, Yedid Hoshen:
You Say Factorization Machine, I Say Neural Network - It's All in the Activation. 389-398
Large-Scale Recommendation
- Lin Ning, Steve Chien, Shuang Song, Mei Chen, Yunqi Xue, Devora Berlowitz:
EANA: Reducing Privacy Risk on Large-scale Recommendation Models. 399-407 - Yingcan Wei, Matthias Langer, Fan Yu, Minseok Lee, Jie Liu, Ji Shi, Zehuan Wang:
A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models. 408-419
Reproducibility Papers
- Sara Latifi, Dietmar Jannach:
Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood. 420-426 - Steffen Rendle, Walid Krichene, Li Zhang, Yehuda Koren:
Revisiting the Performance of iALS on Item Recommendation Benchmarks. 427-435 - Aleksandr V. Petrov, Craig Macdonald:
A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation. 436-447
Industry Papers
- Marjan Celikik, Ana Peleteiro-Ramallo, Jacek Wasilewski:
Reusable Self-Attention Recommender Systems in Fashion Industry Applications. 448-451 - Théo Bontempelli, Benjamin Chapus, François Rigaud, Mathieu Morlon, Marin Lorant, Guillaume Salha-Galvan:
Flow Moods: Recommending Music by Moods on Deezer. 452-455 - Yuyan Wang, Long Tao, Xian-Xing Zhang:
Recommending for a multi-sided marketplace with heterogeneous contents. 456-459 - Andreas Grün, Xenija Neufeld:
Translating the Public Service Media Remit into Metrics and Algorithms. 460-463 - Dmitri Goldenberg, Javier Albert:
Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained Setup. 464-465 - Raul Gomez Bruballa, Lauren Burnham-King, Alessandra Sala:
Learning Users' Preferred Visual Styles in an Image Marketplace. 466-468 - Jan Hartman, Davorin Kopic:
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding. 469-471 - Blaz Skrlj, Adi Schwartz, Jure Ferlez, Davorin Kopic, Naama Ziporin:
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations. 472-475 - Moran Haham:
Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settings. 476-478 - Zachary Harrison, Anish Khazane:
Taxonomic Recommendations of Real Estate Properties with Textual Attribute Information. 479-481 - Dmytro Ivchenko, Dennis Van Der Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, Shahin Sefati:
TorchRec: a PyTorch Domain Library for Recommendation Systems. 482-483 - Naime Ranjbar Kermany, Luiz Pizzato, Thireindar Min, Callum Scott, Anna Leontjeva:
A Multi-Stakeholder Recommender System for Rewards Recommendations. 484-487 - Carlos Carvalheira, Tiago Lacerda, Diogo Gonçalves:
Recommendations: They're in fashion. 488-489 - Petros Katsileros, Nikiforos Mandilaras, Dimitrios Mallis, Vassilis Pitsikalis, Stavros Theodorakis, Gil Chamiel:
An Incremental Learning framework for Large-scale CTR Prediction. 490-493 - Shayak Banerjee, Vijay Pappu, Nilothpal Talukder, Shoya Yoshida, Arnab Bhadury, Allison Schloss, Jasmine Paulino:
Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant Content. 494-498 - Kim Falk, Chen Karako:
Optimizing product recommendations for millions of merchants. 499-501 - Jiajing Xu, Andrew Zhai, Charles Rosenberg:
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach. 502-505 - Chen Luo, William Headden, Neela Avudaiappan, Haoming Jiang, Tianyu Cao, Qingyu Yin, Yifan Gao, Zheng Li, Rahul Goutam, Haiyang Zhang, Bing Yin:
Query Attribute Recommendation at Amazon Search. 506-508 - Siyong Ma, Puja Das, Sofia Maria Nikolakaki, Qifeng Chen, Humeyra Topcu Altintas:
Two-Layer Bandit Optimization for Recommendations. 509-511 - Dirk Sierag, Kevin Zielnicki:
Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders. 512-515 - Ziyang Tang, Yiheng Duan, Steven Zhu, Stephanie Zhang, Lihong Li:
Estimating Long-term Effects from Experimental Data. 516-518 - Eric Paul Nichols, Ruomeng Xu, Balasubramanian Thiagarajan, Shruti Kamath:
Zillow: Volume Governing for Email and Push Messages. 519-521 - Zhou Qin, Wenyang Liu:
Automate Page Layout Optimization: An Offline Deep Q-Learning Approach. 522-524 - Manisha Verma, Shaunak Mishra:
Recommendation Systems for Ad Creation: A View from the Trenches. 525-527 - Lex Beattie, Dan Taber, Henriette Cramer:
Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems. 528-530 - Aditya Joshi, Chin Lin Wong, Diego Marinho de Oliveira, Farhad Zafari, Fernando Mourão, Sabir Ribas, Saumya Pandey:
Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative Filtering. 531-533 - Zehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, Fan Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi, Kunlun Li:
Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference. 534-537 - Yoji Tomita, Riku Togashi, Daisuke Moriwaki:
Matching Theory-based Recommender Systems in Online Dating. 538-541 - Moumita Bhattacharya, Sudarshan Lamkhede:
Augmenting Netflix Search with In-Session Adapted Recommendations. 542-545 - M. Jeffrey Mei, Cole Zuber, Yasaman Khazaeni:
A Lightweight Transformer for Next-Item Product Recommendation. 546-549
Late-Breaking Results
- Antonela Tommasel, Filippo Menczer:
Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders? 550-555 - Bruno Sguerra, Viet-Anh Tran, Romain Hennequin:
Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization. 556-561 - Congcong Liu, Yuejiang Li, Jian Zhu, Fei Teng, Xiwei Zhao, Changping Peng, Zhangang Lin, Jingping Shao:
Position Awareness Modeling with Knowledge Distillation for CTR Prediction. 562-566 - Andres Ferraro, Gustavo Ferreira, Fernando Diaz, Georgina Born:
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship. 567-572 - Walid Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui:
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations. 573-578 - Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards Recommender Systems with Community Detection and Quantum Computing. 579-585 - Ladislav Peska, Stepán Balcar:
The Effect of Feedback Granularity on Recommender Systems Performance. 586-591 - Jessie J. Smith, Lucia Jayne, Robin Burke:
Recommender Systems and Algorithmic Hate. 592-597 - Hossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani, Yashar Deldjoo:
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation. 598-603 - Vojtech Vancura, Rodrigo Alves, Petr Kasalický, Pavel Kordík:
Scalable Linear Shallow Autoencoder for Collaborative Filtering. 604-609 - Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards the Evaluation of Recommender Systems with Impressions. 610-615 - Giuseppe Spillo, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules. 616-621 - Alexey Grishanov, Anastasia Ianina, Konstantin V. Vorontsov:
Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems. 622-627
Demonstrations
- Ivica Kostric, Krisztian Balog, Tølløv Alexander Aresvik, Nolwenn Bernard, Eyvinn Thu Dørheim, Pholit Hantula, Sander Havn-Sørensen, Rune Henriksen, Hengameh Hosseini, Ekaterina Khlybova, Weronika Lajewska, Sindre Ekrheim Mosand, Narmin Orujova:
DAGFiNN: A Conversational Conference Assistant. 628-631 - Karl Higley, Even Oldridge, Ronay Ak, Sara Rabhi, Gabriel de Souza Pereira Moreira:
Building and Deploying a Multi-Stage Recommender System with Merlin. 632-635 - Jan Safarík, Vojtech Vancura, Pavel Kordík:
RepSys: Framework for Interactive Evaluation of Recommender Systems. 636-639 - Joey De Pauw, Koen Ruymbeek, Bart Goethals:
Who do you think I am? Interactive User Modelling with Item Metadata. 640-643 - Behnam Rahdari, Peter Brusilovsky, Daqing He, Khushboo Maulikmihir Thaker, Zhimeng Luo, Young Ji Lee:
HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers. 644-647 - Lien Michiels, Robin Verachtert, Bart Goethals:
RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data. 648-651
Workshops and Challenge
- Eva Zangerle, Christine Bauer, Alan Said:
Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022). 652-653 - Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile:
CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. 654-657 - Himan Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou:
MORS 2022: The Second Workshop on Multi-Objective Recommender Systems. 658-660 - João Vinagre, Marie Al-Ghossein, Alípio Mário Jorge, Albert Bifet, Ladislav Peska:
ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling. 661-662 - Vito Walter Anelli, Pierpaolo Basile, Gerard de Melo, Francesco Maria Donini, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker:
Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). 663-666 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen:
Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'22). 667-670 - Toine Bogers, David Graus, Mesut Kaya, Francisco Gutiérrez, Sepideh Mesbah, Chris Johnson:
Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022). 671-674 - Joseph A. Konstan, Ajith Muralidharan, Ankan Saha, Shilad Sen, Mengting Wan, Longqi Yang:
RecWork: Workshop on Recommender Systems for the Future of Work. 675-677 - Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg, Markus Zanker:
Workshop on Recommenders in Tourism (RecTour). 678-679 - Reza Shirvany, Humberto Jesús Corona Pampín:
Fourth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2022. 680-683 - Richard Liaw, Paige Bailey, Ying Li, Maria Dimakopoulou, Yves Raimond:
REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale. 684-685 - Nasim Sonboli, Toshihiro Kamishima, Amifa Raj, Luca Belli, Robin Burke:
FAccTRec 2022: The 5th Workshop on Responsible Recommendation. 686-687 - Toine Bogers, Cataldo Musto, David (Xuejun) Wang, Alexander Felfernig, Simone Borg Bruun, Giovanni Semeraro, Yong Zheng:
FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services. 688-690 - Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
CARS: Workshop on Context-Aware Recommender Systems 2022. 691-693 - Nick Landia, Frederick Cheung, Donna North, Saikishore Kalloori, Abhishek Srivastava, Bruce Ferwerda:
RecSys Challenge 2022: Fashion Purchase Prediction. 694-697
Tutorials
- Weiwen Liu, Jiarui Qin, Ruiming Tang, Bo Chen:
Neural Re-ranking for Multi-stage Recommender Systems. 698-699 - Christy D. Bergman, Kourosh Hakhamaneshi:
Hands-on Reinforcement Learning for Recommender Systems - From Bandits to SlateQ to Offline RL with Ray RLlib. 700-701 - Francesco Barile, Amra Delic, Ladislav Peska:
Tutorial on Offline Evaluation for Group Recommender Systems. 702-705 - Ronay Ak, Benedikt Schifferer, Sara Rabhi, Gabriel de Souza Pereira Moreira:
Training and Deploying Multi-Stage Recommender Systems. 706-707 - Dmitry Ustalov, Natalia Fedorova, Nikita Pavlichenko:
Improving Recommender Systems with Human-in-the-Loop. 708-709 - Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras:
Hands on Explainable Recommender Systems with Knowledge Graphs. 710-713 - Elisabeth Lex, Markus Schedl:
Psychology-informed Recommender Systems Tutorial. 714-717 - Omprakash Sonie:
Conversational Recommender System Using Deep Reinforcement Learning. 718-719
Doctoral Symposium
- Khushboo Thaker:
KA-Recsys: Knowledge Appropriate Patient Focused Recommendation Technologies. 720-721 - Xinyi Li, Edward C. Malthouse:
An Interpretable Neural Network Model for Bundle Recommendations: Doctoral Symposium, Extended Abstract. 722-723 - Patrik Dokoupil:
Long-term fairness for Group Recommender Systems with Large Groups. 724-726 - Vincenzo Paparella:
Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems. 727-729 - Tendai Mukande:
Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation. 730-734 - Maxwell Szymanski, Katrien Verbert, Vero Vanden Abeele:
Designing and evaluating explainable AI for non-AI experts: challenges and opportunities. 735-736 - Jessie J. Smith:
Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems. 737-738 - Nicolò Felicioni:
Enhancing Counterfactual Evaluation and Learning for Recommendation Systems. 739-741 - Amifa Raj:
Fair Ranking Metrics. 742-743
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