Table of Contents

• An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior ChangesMatús Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Móro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Mária Bielikova. 1-11 [doi]
• The Dual Echo Chamber: Modeling Social Media Polarization for Interventional RecommendingTim Donkers, Jürgen Ziegler 0001. 12-22 [doi]
• I Want to Break Free! Recommending Friends from Outside the Echo ChamberAntonela Tommasel, Juan Manuel Rodriguez, Daniela Godoy. 23-33 [doi]
• Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go HigherHarald Steck, Dawen Liang. 34-43 [doi]
• Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionZhenrui Yue, Zhankui He, Huimin Zeng, Julian J. McAuley. 44-54 [doi]
• Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After AllFlorian Wilhelm. 55-62 [doi]
• Pessimistic Reward Models for Off-Policy Learning in RecommendationOlivier Jeunen, Bart Goethals. 63-74 [doi]
• Towards Unified Metrics for Accuracy and Diversity for Recommender SystemsJavier Parapar, Filip Radlinski. 75-84 [doi]
• Values of User Exploration in Recommender SystemsMinmin Chen, Yuyan Wang, Can Xu, Ya Le, Mohit Sharma, Lee Richardson, Su-Lin Wu, Ed Chi. 85-95 [doi]
• Online Evaluation Methods for the Causal Effect of RecommendationsMasahiro Sato. 96-101 [doi]
• Accordion: A Trainable Simulator forLong-Term Interactive SystemsJames McInerney, Ehtsham Elahi, Justin Basilico, Yves Raimond, Tony Jebara. 102-113 [doi]
• Evaluating the Robustness of Off-Policy EvaluationYuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, Kei Tateno. 114-123 [doi]
• "Serving Each User": Supporting Different Eating Goals Through a Multi-List Recommender InterfaceAlain Starke, Edis Asotic, Christoph Trattner. 124-132 [doi]
• User Bias in Beyond-Accuracy Measurement of Recommendation AlgorithmsNingxia Wang, Li Chen 0009. 133-142 [doi]
• Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based RecommendationGabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, Even Oldridge. 143-153 [doi]
• Sparse Feature Factorization for Recommender Systems with Knowledge GraphsVito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Alberto Carlo Maria Mancino. 154-165 [doi]
• ProtoCF: Prototypical Collaborative Filtering for Few-shot RecommendationAravind Sankar, Junting Wang, Adit Krishnan, Hari Sundaram. 166-175 [doi]
• Towards Source-Aligned Variational Models for Cross-Domain RecommendationAghiles Salah, Thanh-Binh Tran, Hady W. Lauw. 176-186 [doi]
• Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word RepresentationsMarco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 187-198 [doi]
• Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block ModelsGaël Poux-Médard, Julien Velcin, Sabine Loudcher. 199-208 [doi]
• Fast Multi-Step Critiquing for VAE-based Recommender SystemsDiego Antognini, Boi Faltings. 209-219 [doi]
• Large-scale Interactive Conversational Recommendation System using Actor-Critic FrameworkAli Montazeralghaem, James Allan, Philip S. Thomas. 220-229 [doi]
• The role of preference consistency, defaults and musical expertise in users' exploration behavior in a genre exploration recommenderYu Liang, Martijn C. Willemsen. 230-240 [doi]
• Partially Observable Reinforcement Learning for Dialog-based Interactive RecommendationYaxiong Wu, Craig Macdonald, Iadh Ounis. 241-251 [doi]
• Local Factor Models for Large-Scale Inductive RecommendationLongqi Yang, Tobias Schnabel, Paul N. Bennett, Susan T. Dumais. 252-262 [doi]
• cDLRM: Look Ahead Caching for Scalable Training of Recommendation ModelsKeshav Balasubramanian, Abdulla Alshabanah, Joshua D. Choe, Murali Annavaram. 263-272 [doi]
• Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?Daichi Amagata, Takahiro Hara. 273-281 [doi]
• Next-item Recommendations in Short SessionsWenzhuo Song, Shoujin Wang, Yan Wang, Shengsheng Wang. 282-291 [doi]
• Burst-induced Multi-Armed Bandit for Learning RecommendationRodrigo Alves, Antoine Ledent, Marius Kloft. 292-301 [doi]
• Hierarchical Latent Relation Modeling for Collaborative Metric LearningViet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin, Manuel Moussallam. 302-309 [doi]
• Top-K Contextual Bandits with Equity of ExposureOlivier Jeunen, Bart Goethals. 310-320 [doi]
• Debiased Explainable Pairwise Ranking from Implicit FeedbackKhalil Damak, Sami Khenissi, Olfa Nasraoui. 321-331 [doi]
• Privacy Preserving Collaborative Filtering by Distributed MediationAlon Ben Horin, Tamir Tassa. 332-341 [doi]
• Stronger Privacy for Federated Collaborative Filtering With Implicit FeedbackLorenzo Minto, Moritz Haller, Benjamin Livshits, Hamed Haddadi. 342-350 [doi]
• Mitigating Confounding Bias in Recommendation via Information BottleneckDugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, Zhong Ming 0001. 351-360 [doi]
• Learning to Represent Human Motives for Goal-directed Web BrowsingJyun-Yu Jiang, Chia-Jung Lee, Longqi Yang, Bahareh Sarrafzadeh, Brent J. Hecht, Jaime Teevan. 361-371 [doi]
• Debiased Off-Policy Evaluation for Recommendation SystemsYusuke Narita, Shota Yasui, Kohei Yata. 372-379 [doi]
• Follow the guides: disentangling human and algorithmic curation in online music consumptionQuentin Villermet, Jérémie Poiroux, Manuel Moussallam, Thomas Louail, Camille Roth. 380-389 [doi]
• Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat ConsumptionJérémie Rappaz, Julian J. McAuley, Karl Aberer. 390-399 [doi]
• Denoising User-aware Memory Network for RecommendationZhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li. 400-410 [doi]
• Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender SystemsDanni Peng, Sinno Jialin Pan, Jie Zhang, Anxiang Zeng. 411-421 [doi]
• Shared Neural Item Representations for Completely Cold Start ProblemRamin Raziperchikolaei, Guannan Liang, Young-joo Chung. 422-431 [doi]
• A Payload Optimization Method for Federated Recommender SystemsFarwa K. Khan, Adrian Flanagan, Kuan Eeik Tan, Zareen Alamgir, Muhammad Ammad-ud-din. 432-442 [doi]
• Cold Start Similar Artists Ranking with Gravity-Inspired Graph AutoencodersGuillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus, Viet-Anh Tran, Michalis Vazirgiannis. 443-452 [doi]
• Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor NetworkHuiyuan Chen, Yusan Lin, Fei Wang, Hao Yang. 453-462 [doi]
• Semi-Supervised Visual Representation Learning for Fashion CompatibilityAmbareesh Revanur, Vijay Kumar, Deepthi Sharma. 463-472 [doi]
• Large-Scale Modeling of Mobile User Click Behaviors Using Deep LearningXin Zhou, Yang Li. 473-483 [doi]
• EX3: Explainable Attribute-aware Item-set RecommendationsYikun Xian, Tong Zhao, Jin Li 0003, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan 0001, Yongfeng Zhang. 484-494 [doi]
• Page-level Optimization of e-Commerce Item RecommendationsChieh Lo, Hongliang Yu, Xin Yin, Krutika Shetty, Changchen He, Kathy Hu, Justin M. Platz, Adam Ilardi, Sriganesh Madhvanath. 495-504 [doi]
• A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation ModelsAlexander Dallmann, Daniel Zoller, Andreas Hotho. 505-514 [doi]
• Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric ComparisonAhtsham Manzoor, Dietmar Jannach. 515-520 [doi]
• Reenvisioning the comparison between Neural Collaborative Filtering and Matrix FactorizationVito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo. 521-529 [doi]
• AIR: Personalized Product Recommender System for Nike's Digital TransformationSteven Essinger, Dave Huber, Daniel Tang. 530-532 [doi]
• Boosting Local Recommendations With Partially Trained Global ModelYuxi Zhang, Kexin Xie. 533-535 [doi]
• Building a Platform for Ensemble-based Personalized Research Literature Recommendations for AI and Data Science at Zeta AlphaJakub Zavrel, Artem Grotov, Jonathan Mitnik. 536-537 [doi]
• Building Public Service Recommenders: Logbook of a JourneyChristina Boididou, Di Sheng, Felix J. Mercer Moss, Alessandro Piscopo. 538-540 [doi]
• Challenges Experienced in Public Service Media Recommendation SystemsAndreas Grün, Xenija Neufeld. 541-544 [doi]
• Content-based book recommendations: Personalised and explainable recommendations without the cold-start problemNiels Bogaards, Frederique Schut. 545-547 [doi]
• Drug Discovery as a Recommendation Problem: Challenges and Complexities in Biological DecisionsAnna Gogleva, Eliseo Papa, Erik Jansson, Greet De Baets. 548-550 [doi]
• Exploration in Recommender SystemsMinmin Chen. 551-553 [doi]
• Fairness in Reviewer Recommendations at ElsevierDaniel James Kershaw, Rob Koeling, Stephan Bourgeois, Antonio Trenta, Harriet J. Muncey. 554-555 [doi]
• FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems ResearchSimen Eide, David S. Leslie, Arnoldo Frigessi, Joakim Rishaug, Helge Jenssen, Sofie Verrewaere. 556-558 [doi]
• Jointly Optimize Capacity, Latency and Engagement in Large-scale Recommendation SystemsHitesh Khandelwal, Viet Ha-Thuc, Avishek Dutta, Yining Lu, Nan Du, Zhihao Li, Qi Hu. 559-561 [doi]
• Learning a Voice-based Conversational Recommender using Offline Policy OptimizationFrançois Mairesse, Zhonghao Luo, Tao Ye. 562-564 [doi]
• Learning to Match Job Candidates Using Multilingual Bi-Encoder BERTDor Lavi. 565-566 [doi]
• Offline Evaluation Standards for Recommender SystemsChin Lin Wong, Diego De Oliveira, Farhad Zafari, Fernando Mourão, Rafael Colares, Sabir Ribas. 567-568 [doi]
• Online Learning for Recommendations at GrubhubAlex Egg. 569-571 [doi]
• Personalised Outfit Recommendations: Use Cases, Challenges and OpportunitiesNick Landia. 572-574 [doi]
• Personalizing Peloton: Combining Rankers and Filters To Balance Engagement and Business GoalsShayak Banerjee, Arnab Bhadury, Nilothpal Talukder, Santosh Thammana. 575-576 [doi]
• Recommendations and Results Organization in Netflix SearchSudarshan Dnyaneshwar Lamkhede, Christoph Kofler. 577-579 [doi]
• Recommendations at VideolandMateo Gutierrez Granada, Daan Odijk. 580-582 [doi]
• Recommender Systems for Personalized User Experience: Lessons learned at Booking.comIoannis Kangas, Maud Schwoerer, Lucas J. Bernardi. 583-586 [doi]
• Recommenders in Banking: An End-to-end Personalization Pipeline within INGCarlos Vaquero-Patricio, Nikki Van Ommeren, Santiago Gil-Begue. 587-589 [doi]
• RecSysOps: Best Practices for Operating a Large-Scale Recommender SystemMohammad Saberian, Justin Basilico. 590-591 [doi]
• Scaling Enterprise Recommender Systems for DecentralizationMaurits van der Goes. 592-594 [doi]
• Scaling TensorFlow to 300 million predictions per secondJan Hartman, Davorin Kopic. 595-597 [doi]
• You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open StackJacopo Tagliabue. 598-600 [doi]
• Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl. 601-606 [doi]
• A Constrained Optimization Approach for Calibrated RecommendationsSinan Seymen, Himan Abdollahpouri, Edward C. Malthouse. 607-612 [doi]
• An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion PredictionConor O'Brien, Kin Sum Liu, James Neufeld, Rafael Barreto, Jonathan J. Hunt. 613-619 [doi]
• An Interpretable Recommendation Model for Gerontological CareAndre Paulino de Lima, Laurentino Augusto Dantas, Marcelo Garcia Manzato, Maria da Graça Campos Pimentel, Brunela Orlandi, Paula Castro. 620-626 [doi]
• Auditing the Effect of Social Network Recommendations on Polarization in Geometrical Ideological SpacesPedro Ramaciotti Morales, Jean-Philippe Cointet. 627-632 [doi]
• Automatic Collection Creation and RecommendationSanidhya Singal, Piyush Singh, Manjeet Dahiya. 633-638 [doi]
• Baby Shark to Barracuda: Analyzing Children's Music Listening BehaviorLawrence Spear, Ashlee Milton, Garrett Allen, Amifa Raj, Michael Green, Michael D. Ekstrand, Maria Soledad Pera. 639-644 [doi]
• Do Users Appreciate Explanations of Recommendations? An Analysis in the Movie DomainThi Ngoc Trang Tran, Viet Man Le, Muesluem Atas, Alexander Felfernig, Martin Stettinger, Andrei Popescu 0007. 645-650 [doi]
• Dynamic Graph Construction for Improving Diversity of RecommendationRui Ye, Yuqing Hou, Te Lei, Yunxing Zhang, Qing Zhang, Jiale Guo, Huaiwen Wu, Hengliang Luo. 651-655 [doi]
• Eigenvalue Perturbation for Item-based Recommender SystemsCesare Bernardis, Paolo Cremonesi. 656-660 [doi]
• Estimating and Penalizing Preference Shift in Recommender SystemsMicah Carroll, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan. 661-667 [doi]
• FR-FMSS: Federated Recommendation via Fake Marks and Secret SharingZhaohao Lin, Weike Pan, Zhong Ming 0001. 668-673 [doi]
• Global-Local Item Embedding for Temporal Set PredictionSeungjae Jung, Young-Jin Park, Jisu Jeong, Kyung Min Kim, Hiun Kim, Minkyu Kim, Hanock Kwak. 674-679 [doi]
• Horizontal Cross-Silo Federated Recommender SystemsSaikishore Kalloori, Severin Klingler. 680-684 [doi]
• Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative FilteringYuhi Kawakami, Mahito Sugiyama. 685-690 [doi]
• Optimizing the Selection of Recommendation Carousels with Quantum ComputingMaurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi. 691-696 [doi]
• Play It Again, Sam! Recommending Familiar Music in Fresh WaysGiovanni Gabbolini, Derek Bridge. 697-701 [doi]
• Predicting Music Relistening Behavior Using the ACT-R FrameworkMarkus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex. 702-707 [doi]
• Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?Yan-Martin Tamm, Rinchin Damdinov, Alexey Vasilev. 708-713 [doi]
• Sequence Adaptation via Reinforcement Learning in Recommender SystemsStefanos Antaris, Dimitrios Rafailidis. 714-718 [doi]
• Siamese Neural Networks for Content-based Cold-Start Music RecommendationMichael Pulis, Josef Bajada. 719-723 [doi]
• Soliciting User Preferences in Conversational Recommender Systems via Usage-related QuestionsIvica Kostric, Krisztian Balog, Filip Radlinski. 724-729 [doi]
• The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation LearningVito Walter Anelli, Tommaso Di Noia, Felice Antonio Merra. 730-735 [doi]
• Transfer Learning in Collaborative Recommendation for Bias ReductionZinan Lin, Dugang Liu, Weike Pan, Zhong Ming 0001. 736-740 [doi]
• A Low-Code Tool Supporting the Development of Recommender SystemsClaudio Di Sipio, Juri Di Rocco, Davide Di Ruscio, Phuong Thanh Nguyen. 741-744 [doi]
• Connecting Students with Research Advisors Through User-Controlled RecommendationBehnam Rahdari, Peter Brusilovsky, Alireza Javadian Sabet. 745-748 [doi]
• DataHunter: A System for Finding Datasets Based on Scientific Problem DescriptionsMichael Färber 0001, Ann-Kathrin Leisinger. 749-752 [doi]
• EntityBot: Supporting Everyday Digital Tasks with Entity RecommendationsVuong Thanh Tung, Salvatore Andolina, Giulio Jacucci, Pedram Daee, Khalil Klouche, Mats Sjöberg, Tuukka Ruotsalo, Samuel Kaski. 753-756 [doi]
• Generic Automated Lead Ranking in Dynamics CRMRoyi Ronen, Hilik Berezin, Rotem Preizler, Gopal Kasturi, A. J. Ezzour, Sayalee Bhanavase, Edan Hauon, Oron Nir. 757-759 [doi]
• Multi-Step Critiquing User Interface for Recommender SystemsDiana Andreea Petrescu, Diego Antognini, Boi Faltings. 760-763 [doi]
• NU: BRIEF - A Privacy-aware Newsletter Personalization Engine for PublishersErnesto Diaz-Aviles, Claudia Orellana-Rodriguez, Igor Brigadir, Reshma Narayanan Kutty. 764-767 [doi]
• V-Elliot: Design, Evaluate and Tune Visual Recommender SystemsVito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia. 768-771 [doi]
• 9th International Workshop on News Recommendation and AnalyticsÖzlem Özgöbek, Andreas Lommatzsch, Benjamin Kille, Peng Liu 0025, Zhixin Pu, Jon Atle Gulla. 772-774 [doi]
• ComplexRec 2021: Fifth Workshop on Recommendation in Complex EnvironmentsHiman Abdollahpouri, Toine Bogers, Bamshad Mobasher, Casper Petersen, Maria Soledad Pera. 775-777 [doi]
• FAccTRec 2021: The 4th Workshop on Responsible RecommendationMichael D. Ekstrand, Pierre-Nicolas Schwab, Toshihiro Kamishima, Nasim Sonboli. 778-779 [doi]
• GReS: Workshop on Graph Neural Networks for Recommendation and SearchThibaut Thonet, Stéphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean-Michel Renders, Michael M. Bronstein. 780-782 [doi]
• Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'21)Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Elisabeth Lex, Pasquale Lops, Giovanni Semeraro, Martijn C. Willemsen. 783-786 [doi]
• MORS 2021: 1st Workshop on Multi-Objective Recommender SystemsHiman Abdollahpouri, Mehdi Elahi, Masoud Mansoury, Shaghayegh Sahebi, Zahra Nazari, Allison Chaney, Babak Loni. 787-788 [doi]
• OHARS: Second Workshop on Online Misinformation- and Harm-Aware Recommender SystemsAntonela Tommasel, Daniela Godoy, Arkaitz Zubiaga. 789-791 [doi]
• ORSUM 2021 - 4th Workshop on Online Recommender Systems and User ModelingJoão Vinagre, Alípio Mário Jorge, Marie Al-Ghossein, Albert Bifet. 792-793 [doi]
• Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES)Eva Zangerle, Christine Bauer 0001, Alan Said. 794-795 [doi]
• PodRecs 2021: 2nd Workshop on Podcast RecommendationsChing-Wei Chen, Rosie Jones, Zahra Nazari, Longqi Yang, Maria Eskevich, Gareth James Francis Jones, Sergio Oramas. 796-798 [doi]
• RecSys in HR: Workshop on Recommender Systems for Human ResourcesToine Bogers, David Graus, Mesut Kaya, Francisco Gutiérrez, Katrien Verbert. 799-802 [doi]
• SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems ResearchMichael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom. 803-805 [doi]
• Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Vito Walter Anelli, Pierpaolo Basile, Tommaso Di Noia, Francesco M. Donini, Cataldo Musto, Fedelucio Narducci, Markus Zanker. 806-809 [doi]
• Workshop on Recommender Systems in Fashion and RetailShatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany. 810-812 [doi]
• Workshop on Context-Aware Recommender Systems (CARS) 2021Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci 0001, Alexander Tuzhilin, Moshe Unger. 813-814 [doi]
• Workshop on Recommenders in Tourism (RecTour)Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Markus Zanker. 815-816 [doi]
• XMRec: Workshop on Cross-Market RecommendationMohammad Aliannejadi, Hamed R. Bonab, Ali Vardasbi, Evangelos Kanoulas, James Allan, Vanessa Murdock. 817-818 [doi]
• RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter's Home TimelineVito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain 0001, Yuanpu Xie, Jonathan Hunt, Michael M. Bronstein, Wenzhe Shi. 819-824 [doi]
• Bias Issues and Solutions in Recommender System: Tutorial on the RecSys 2021Jiawei Chen, Xiang Wang, Fuli Feng, Xiangnan He 0001. 825-827 [doi]
• Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent AdvancesYuta Saito, Thorsten Joachims. 828-830 [doi]
• End-to-End Session-Based Recommendation on GPUGabriel de Souza Pereira Moreira, Sara Rabhi, Ronay Ak, Benedikt Schifferer. 831-833 [doi]
• Multi-Modal Recommender Systems: Hands-On ExplorationQuoc-Tuan Truong, Aghiles Salah, Hady W. Lauw. 834-837 [doi]
• Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to ApplicationsVito Walter Anelli, Luca Belli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci, Claudio Pomo. 838-841 [doi]
• RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and EvaluationWenqiang Lei, Chongming Gao, Maarten de Rijke. 842-844 [doi]
• Argument-based generation and explanation of recommendationsAndrés Segura-Tinoco. 845-850 [doi]
• An Ontology-based Knowledgebase for User Profile and Garment Features in Apparel Recommender SystemsBolanle Olufisayo Dahunsi. 851-854 [doi]
• Biases in Recommendation SystemSaumya Bhadani. 855-859 [doi]
• Learning Dynamic Insurance Recommendations from Users' Click SessionsSimone Borg Bruun. 860-863 [doi]
• Leveraging Multi-Faceted User Preferences for Improving Click-Through Rate PredictionsPan Li. 864-868 [doi]
• Measuring and Mitigating Bias and Harm in Personalized AdvertisingMuhammad Ali. 869-872 [doi]
• Modeling Users and Items for Recommenders: There Is More than SemanticsMete Sertkan. 873-877 [doi]
• Neural Basket Embedding for Sequential RecommendationVojtech Vancura. 878-883 [doi]