Table of Contents

• "My AI must have been broken": How AI Stands to Reshape Human CommunicationMor Naaman. 1 [doi]
• Co-designing ML Models with Data ActivistsCatherine D'Ignazio. 2 [doi]
• Exploring the longitudinal effects of nudging on users' music genre exploration behavior and listening preferencesYu Liang, Martijn C. Willemsen. 3-13 [doi]
• Modeling User Repeat Consumption Behavior for Online Novel RecommendationYuncong Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Jing Cai, leeven luo, Sheng-hua Zhong. 14-24 [doi]
• A User-Centered Investigation of Personal Music ToursGiovanni Gabbolini, Derek G. Bridge. 25-34 [doi]
• Towards Psychologically-Grounded Dynamic Preference ModelsMihaela Curmei, Andreas A. Haupt, Benjamin Recht, Dylan Hadfield-Menell. 35-48 [doi]
• Aspect Re-distribution for Learning Better Item Embeddings in Sequential RecommendationWei Cai, Weike Pan, Jingwen Mao, Zhechao Yu, Congfu Xu. 49-58 [doi]
• Defending Substitution-Based Profile Pollution Attacks on Sequential RecommendersZhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang. 59-70 [doi]
• Context and Attribute-Aware Sequential Recommendation via Cross-AttentionAhmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme. 71-80 [doi]
• Effective and Efficient Training for Sequential Recommendation using Recency SamplingAleksandr Petrov, Craig Macdonald. 81-91 [doi]
• Denoising Self-Attentive Sequential RecommendationHuiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, Hao Yang. 92-101 [doi]
• Modeling Two-Way Selection Preference for Person-Job FitChen Yang, Yupeng Hou, Yang Song, Tao Zhang, Ji-Rong Wen, Wayne Xin Zhao. 102-112 [doi]
• Learning Recommendations from User Actions in the Item-poor Insurance DomainSimone Borg Bruun, Maria Maistro, Christina Lioma. 113-123 [doi]
• Multi-Modal Dialog State Tracking for Interactive Fashion RecommendationYaxiong Wu 0001, Craig Macdonald, Iadh Ounis. 124-133 [doi]
• Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit StrategyMaryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed A. Aslam. 134-144 [doi]
• Countering Popularity Bias by Regularizing Score DifferencesWondo Rhee, Sung-Min Cho, Bongwon Suh. 145-155 [doi]
• Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data HeterogeneityKiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu. 156-167 [doi]
• Fairness-aware Federated Matrix FactorizationShuchang Liu 0001, Yingqiang Ge, Shuyuan Xu, Yongfeng Zhang, Amélie Marian. 168-178 [doi]
• Dynamic Global Sensitivity for Differentially Private Contextual BanditsHuazheng Wang, David Zhao, Hongning Wang. 179-187 [doi]
• Don't recommend the obvious: estimate probability ratiosRoberto Pellegrini, WenJie Zhao, Iain Murray. 188-197 [doi]
• Solving Diversity-Aware Maximum Inner Product Search Efficiently and EffectivelyKohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara. 198-207 [doi]
• RADio - Rank-Aware Divergence Metrics to Measure Normative Diversity in News RecommendationsSanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, Maarten de Rijke. 208-219 [doi]
• Reducing Cross-Topic Political Homogenization in Content-Based News RecommendationKarthik Shivaram, Ping Liu 0002, Matthew A. Shapiro, Mustafa Bilgic 0001, Aron Culotta. 220-228 [doi]
• Exploiting Negative Preference in Content-based Music Recommendation with Contrastive LearningMinju Park, Kyogu Lee. 229-236 [doi]
• BRUCE: Bundle Recommendation Using Contextualized item EmbeddingsTzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, Mark Last. 237-245 [doi]
• ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable RecommendationsAlessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, Markus Schedl. 246-256 [doi]
• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender SystemsHuiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang. 257-267 [doi]
• Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User IntentionsWeixin Chen, Mingkai He, Yongxin Ni, Weike Pan, Li Chen, Zhong Ming 0001. 268-277 [doi]
• CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce EnvironmentRui Ma, Ning Liu, Jingsong Yuan, Huafeng Yang, Jiandong zhang. 278-287 [doi]
• Bundle MCR: Towards Conversational Bundle RecommendationZhankui He, Handong Zhao, Tong Yu 0001, SungChul Kim, Fan Du, Julian J. McAuley. 288-298 [doi]
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)Shijie Geng, Shuchang Liu 0001, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang. 299-315 [doi]
• Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase RecommendationOri Katz, Oren Barkan, Noam Koenigstein, Nir Zabari. 316-326 [doi]
• Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with RationalesShuyang Li, Bodhisattwa Prasad Majumder, Julian J. McAuley. 327-337 [doi]
• Off-Policy Actor-critic for Recommender SystemsMinmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi. 338-349 [doi]
• Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree SearchDilina Chandika Rajapakse, Douglas Leith. 350-359 [doi]
• MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-TransformerBhumika, Debasis Das 0001. 360-368 [doi]
• Adversary or Friend? An adversarial Approach to Improving Recommender SystemsPannaga Shivaswamy, Dario García-García. 369-377 [doi]
• Dual Attentional Higher Order Factorization MachinesArindam Sarkar, Dipankar Das, Vivek Sembium, Prakash Mandayam Comar. 378-388 [doi]
• You Say Factorization Machine, I Say Neural Network - It's All in the ActivationChen Almagor, Yedid Hoshen. 389-398 [doi]
• EANA: Reducing Privacy Risk on Large-scale Recommendation ModelsLin Ning, Steve Chien, Shuang Song 0001, Mei Chen, Yunqi Xue, Devora Berlowitz. 399-407 [doi]
• A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation ModelsYingcan Wei, Matthias Langer, Fan Yu, Minseok Lee, Jie Liu, Ji Shi, Zehuan Wang. 408-419 [doi]
• Streaming Session-Based Recommendation: When Graph Neural Networks meet the NeighborhoodSara Latifi, Dietmar Jannach. 420-426 [doi]
• Revisiting the Performance of iALS on Item Recommendation BenchmarksSteffen Rendle, Walid Krichene, Li Zhang, Yehuda Koren. 427-435 [doi]
• A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationAleksandr Petrov, Craig Macdonald. 436-447 [doi]
• Reusable Self-Attention Recommender Systems in Fashion Industry ApplicationsMarjan Celikik, Ana Peleteiro-Ramallo, Jacek Wasilewski. 448-451 [doi]
• Flow Moods: Recommending Music by Moods on DeezerThéo Bontempelli, Benjamin Chapus, François Rigaud, Mathieu Morlon, Marin Lorant, Guillaume Salha-Galvan. 452-455 [doi]
• Recommending for a multi-sided marketplace with heterogeneous contentsYuyan Wang, Long Tao, Xian-Xing Zhang. 456-459 [doi]
• Translating the Public Service Media Remit into Metrics and AlgorithmsAndreas Grün, Xenija Neufeld. 460-463 [doi]
• Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained SetupDmitri Goldenberg, Javier Albert. 464-465 [doi]
• Learning Users' Preferred Visual Styles in an Image MarketplaceRaul Gomez Bruballa, Lauren Burnham-King, Alessandra Sala. 466-468 [doi]
• Exploration with Model Uncertainty at Extreme Scale in Real-Time BiddingJan Hartman, Davorin Kopic. 469-471 [doi]
• Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online RecommendationsBlaz Skrlj, Adi Schwartz, Jure Ferlez, Davorin Kopic, Naama Ziporin. 472-475 [doi]
• Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settingsmoran haham. 476-478 [doi]
• Taxonomic Recommendations of Real Estate Properties with Textual Attribute InformationZachary Harrison, Anish Khazane. 479-481 [doi]
• TorchRec: a PyTorch Domain Library for Recommendation SystemsDmytro Ivchenko, Dennis Van Der Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, Shahin Sefati. 482-483 [doi]
• A Multi-Stakeholder Recommender System for Rewards RecommendationsNaime Ranjbar Kermany, Luiz Pizzato, Thireindar Min, Callum Scott, Anna Leontjeva. 484-487 [doi]
• Recommendations: They're in fashionCarlos Carvalheira, Tiago Lacerda, Diogo Gonçalves 0007. 488-489 [doi]
• An Incremental Learning framework for Large-scale CTR PredictionPetros Katsileros, Nikiforos Mandilaras, Dimitrios Mallis, Vassilis Pitsikalis, Stavros Theodorakis, Gil Chamiel. 490-493 [doi]
• Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant ContentShayak Banerjee, Vijay Pappu, Nilothpal Talukder, Shoya Yoshida, Arnab Bhadury, Allison Schloss, Jasmine Paulino. 494-498 [doi]
• Optimizing product recommendations for millions of merchantsKim Falk, Chen Karako. 499-501 [doi]
• Rethinking Personalized Ranking at Pinterest: An End-to-End ApproachJiajing Xu, Andrew Zhai, Charles Rosenberg. 502-505 [doi]
• Query Attribute Recommendation at Amazon SearchChen Luo, William Headden, Neela Avudaiappan, Haoming Jiang, Tianyu Cao, Qingyu Yin, Yifan Gao 0001, Zheng Li, Rahul Goutam, Haiyang Zhang, Bing Yin. 506-508 [doi]
• Two-Layer Bandit Optimization for RecommendationsSiyong Ma, Puja Das, Sofia Maria Nikolakaki, Qifeng Chen, Humeyra Topcu-Altintas. 509-511 [doi]
• Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked EncodersDirk Sierag, Kevin Zielnicki. 512-515 [doi]
• Estimating Long-term Effects from Experimental DataZiyang Tang, Yiheng Duan, Steven Zhu, Stephanie Zhang, Lihong Li 0001. 516-518 [doi]
• Zillow: Volume Governing for Email and Push MessagesEric Paul Nichols, Ruomeng Xu, Balasubramanian Thiagarajan, Shruti Kamath. 519-521 [doi]
• Automate Page Layout Optimization: An Offline Deep Q-Learning ApproachZhou Qin, Wenyang Liu. 522-524 [doi]
• Recommendation Systems for Ad Creation: A View from the TrenchesManisha Verma, Shaunak Mishra. 525-527 [doi]
• Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation SystemsLex Beattie, Dan Taber, Henriette Cramer. 528-530 [doi]
• Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative FilteringAditya Joshi, Chin Lin Wong, Diego Marinho de Oliveira, Farhad Zafari, Fernando Mourão, Sabir Ribas, Saumya Pandey. 531-533 [doi]
• Merlin HugeCTR: GPU-accelerated Recommender System Training and InferenceZehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, Fan Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi, Kunlun Li. 534-537 [doi]
• Matching Theory-based Recommender Systems in Online DatingYoji Tomita, Riku Togashi, Daisuke Moriwaki. 538-541 [doi]
• Augmenting Netflix Search with In-Session Adapted RecommendationsMoumita Bhattacharya, Sudarshan Lamkhede. 542-545 [doi]
• A Lightweight Transformer for Next-Item Product RecommendationM. Jeffrey Mei, Cole Zuber, Yasaman Khazaeni. 546-549 [doi]
• Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?Antonela Tommasel, Filippo Menczer. 550-555 [doi]
• Discovery Dynamics: Leveraging Repeated Exposure for User and Music CharacterizationBruno Sguerra, Viet-Anh Tran, Romain Hennequin. 556-561 [doi]
• Position Awareness Modeling with Knowledge Distillation for CTR PredictionCongcong Liu, Yuejiang Li, Jian Zhu, Fei Teng, Xiwei Zhao, Changping Peng, Zhangang Lin, JingPing Shao. 562-566 [doi]
• Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural CitizenshipAndres Ferraro, Gustavo Ferreira, Fernando Diaz 0001, Georgina Born. 567-572 [doi]
• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based RecommendationsWalid Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui. 573-578 [doi]
• Towards Recommender Systems with Community Detection and Quantum ComputingRiccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi. 579-585 [doi]
• The Effect of Feedback Granularity on Recommender Systems PerformanceLadislav Peska, Stepán Balcar. 586-591 [doi]
• Recommender Systems and Algorithmic HateJessie J. Smith, Lucia Jayne, Robin Burke. 592-597 [doi]
• Exploring the Impact of Temporal Bias in Point-of-Interest RecommendationHossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani, Yashar Deldjoo. 598-603 [doi]
• Scalable Linear Shallow Autoencoder for Collaborative FilteringVojtech Vancura, Rodrigo Alves, Petr Kasalický, Pavel Kordík. 604-609 [doi]
• Towards the Evaluation of Recommender Systems with ImpressionsFernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi. 610-615 [doi]
• Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical RulesGiuseppe Spillo, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 616-621 [doi]
• Multiobjective Evaluation of Reinforcement Learning Based Recommender SystemsAlexey Grishanov, Anastasia Ianina, Konstantin V. Vorontsov. 622-627 [doi]
• DAGFiNN: A Conversational Conference AssistantIvica 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. 628-631 [doi]
• Building and Deploying a Multi-Stage Recommender System with MerlinKarl Higley, Even Oldridge, Ronay Ak, Sara Rabhi, Gabriel de Souza Pereira Moreira. 632-635 [doi]
• RepSys: Framework for Interactive Evaluation of Recommender SystemsJan Safarík, Vojtech Vancura, Pavel Kordík. 636-639 [doi]
• Who do you think I am? Interactive User Modelling with Item MetadataJoey De Pauw, Koen Ruymbeek, Bart Goethals. 640-643 [doi]
• HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and CaregiversBehnam Rahdari, Peter Brusilovsky, Daqing He, Khushboo Maulikmihir Thaker, Zhimeng Luo, Young Ji Lee. 644-647 [doi]
• RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback DataLien Michiels, Robin Verachtert, Bart Goethals. 648-651 [doi]
• Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022)Eva Zangerle, Christine Bauer 0001, Alan Said. 652-653 [doi]
• CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender SystemsOlivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile. 654-657 [doi]
• MORS 2022: The Second Workshop on Multi-Objective Recommender SystemsHiman Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou. 658-660 [doi]
• ORSUM 2022 - 5th Workshop on Online Recommender Systems and User ModelingJoão Vinagre, Marie Al-Ghossein, Alípio Mário Jorge, Albert Bifet, Ladislav Peska. 661-662 [doi]
• Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Vito Walter Anelli, Pierpaolo Basile, Gerard de Melo, Francesco Maria Donini, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker. 663-666 [doi]
• Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'22)Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen. 667-670 [doi]
• Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)Toine Bogers, David Graus, Mesut Kaya, Francisco Gutiérrez, Sepideh Mesbah, Chris Johnson. 671-674 [doi]
• RecWork: Workshop on Recommender Systems for the Future of WorkJoseph A. Konstan, Ajith Muralidharan, Ankan Saha, Shilad Sen, Mengting Wan, Longqi Yang. 675-677 [doi]
• Workshop on Recommenders in Tourism (RecTour)Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg, Markus Zanker. 678-679 [doi]
• Fourth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2022Reza Shirvany, Humberto Jesús Corona Pampín. 680-683 [doi]
• REVEAL 2022: Reinforcement Learning-Based Recommender Systems at ScaleRichard Liaw, Paige Bailey, Ying Li, Maria Dimakopoulou, Yves Raimond. 684-685 [doi]
• FAccTRec 2022: The 5th Workshop on Responsible RecommendationNasim Sonboli, Toshihiro Kamishima, Amifa Raj, Luca Belli, Robin Burke. 686-687 [doi]
• FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial ServicesToine Bogers, Cataldo Musto, David Wang, Alexander Felfernig, Simone Borg Bruun, Giovanni Semeraro, Yong Zheng 0001. 688-690 [doi]
• CARS: Workshop on Context-Aware Recommender Systems 2022Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci 0001, Alexander Tuzhilin, Moshe Unger. 691-693 [doi]
• RecSys Challenge 2022: Fashion Purchase PredictionNick Landia, Frederick Cheung, Donna North, Saikishore Kalloori, Abhishek Srivastava, Bruce Ferwerda. 694-697 [doi]
• Neural Re-ranking for Multi-stage Recommender SystemsWeiwen Liu, Jiarui Qin, Ruiming Tang, Bo Chen 0023. 698-699 [doi]
• Hands-on Reinforcement Learning for Recommender Systems - From Bandits to SlateQ to Offline RL with Ray RLlibChristy D. Bergman, Kourosh Hakhamaneshi. 700-701 [doi]
• Tutorial on Offline Evaluation for Group Recommender SystemsFrancesco Barile, Amra Delic, Ladislav Peska. 702-705 [doi]
• Training and Deploying Multi-Stage Recommender SystemsRonay Ak, Benedikt Schifferer, Sara Rabhi, Gabriel de Souza Pereira Moreira. 706-707 [doi]
• Improving Recommender Systems with Human-in-the-LoopDmitry Ustalov, Natalia Fedorova, Nikita Pavlichenko. 708-709 [doi]
• Hands on Explainable Recommender Systems with Knowledge GraphsGiacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras. 710-713 [doi]
• Psychology-informed Recommender Systems TutorialElisabeth Lex, Markus Schedl. 714-717 [doi]
• Conversational Recommender System Using Deep Reinforcement LearningOmprakash Sonie. 718-719 [doi]
• KA-Recsys: Knowledge Appropriate Patient Focused Recommendation TechnologiesKhushboo Thaker. 720-721 [doi]
• An Interpretable Neural Network Model for Bundle Recommendations: Doctoral Symposium, Extended AbstractXinyi Li, Edward C. Malthouse. 722-723 [doi]
• Long-term fairness for Group Recommender Systems with Large GroupsPatrik Dokoupil. 724-726 [doi]
• Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender SystemsVincenzo Paparella. 727-729 [doi]
• Heterogeneous Graph Representation Learning for multi-target Cross-Domain RecommendationTendai Mukande. 730-734 [doi]
• Designing and evaluating explainable AI for non-AI experts: challenges and opportunitiesMaxwell Szymanski, Katrien Verbert, Vero Vanden Abeele. 735-736 [doi]
• Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender SystemsJessie J. Smith. 737-738 [doi]
• Enhancing Counterfactual Evaluation and Learning for Recommendation SystemsNicolò Felicioni. 739-741 [doi]
• Fair Ranking MetricsAmifa Raj. 742-743 [doi]