| 2024 | "More to Read" at the Los Angeles Times: Solving a Cold Start Problem with LLMs to Improve Story Discovery. Franklin Horn, Aurelia Alston, Won J. You |
| 2024 | 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24). Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen |
| 2024 | 12th International Workshop on News Recommendation and Analytics (INRA'24). Benjamin Kille, Andreas Lommatzsch, Célina Treuillier, Vandana Yadav, Özlem Özgöbek |
| 2024 | A Comparative Analysis of Text-Based Explainable Recommender Systems. Alejandro Ariza-Casabona, Ludovico Boratto, Maria Salamó |
| 2024 | A Dataset for Adapting Recommender Systems to the Fashion Rental Economy. Karl Audun Kagnes Borgersen, Morten Goodwin, Morten Grundetjern, Jivitesh Sharma |
| 2024 | A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce. Guangtao Nie, Rong Zhi, Xiaofan Yan, Yufan Du, Xiangyang Zhang, Jianwei Chen, Mi Zhou, Hongshen Chen, Tianhao Li, Ziguang Cheng, Sulong Xu, Jinghe Hu |
| 2024 | A Multi-modal Modeling Framework for Cold-start Short-video Recommendation. Gaode Chen, Ruina Sun, Yuezihan Jiang, Jiangxia Cao, Qi Zhang, Jingjian Lin, Han Li, Kun Gai, Xinghua Zhang |
| 2024 | A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios. Christian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, Markus Schedl |
| 2024 | A New Perspective in Health Recommendations: Integration of Human Pose Estimation. Gaetano Dibenedetto |
| 2024 | A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio |
| 2024 | A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics. Junting Wang, Praneet Rathi, Hari Sundaram |
| 2024 | A Tool for Explainable Pension Fund Recommendations using Large Language Models. Eduardo Alves da Silva, Leandro Balby Marinho, Edleno Silva de Moura, Altigran Soares da Silva |
| 2024 | A Tutorial on Feature Interpretation in Recommender Systems. Zhaocheng Du, Chuhan Wu, Qinglin Jia, Jieming Zhu, Xu Chen |
| 2024 | A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation. Zixuan Yi, Iadh Ounis |
| 2024 | AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations. Jan Hartman, Hitesh Sagtani, Julie Tibshirani, Rishabh Mehrotra |
| 2024 | AI-based Human-Centered Recommender Systems: Empirical Experiments and Research Infrastructure. Ruixuan Sun |
| 2024 | AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising. Yang Yang, Bo Chen, Chenxu Zhu, Menghui Zhu, Xinyi Dai, Huifeng Guo, Muyu Zhang, Zhenhua Dong, Ruiming Tang |
| 2024 | AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders. Elizabeth Gómez, David Contreras, Ludovico Boratto, Maria Salamó |
| 2024 | Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems. Yunfan Wu, Qi Cao, Shuchang Tao, Kaike Zhang, Fei Sun, Huawei Shen |
| 2024 | Adaptive Fusion of Multi-View for Graph Contrastive Recommendation. Mengduo Yang, Yi Yuan, Jie Zhou, Meng Xi, Xiaohua Pan, Ying Li, Yangyang Wu, Jinshan Zhang, Jianwei Yin |
| 2024 | AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation. Michael D. Ekstrand, Maria Soledad Pera, Alan Said |
| 2024 | Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models. Jaidev Shah, Gang Luo, Jialin Liu, Amey Barapatre, Fan Wu, Chuck Wang, Hongzhi Li |
| 2024 | Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating Explainers. Amir Reza Mohammadi, Andreas Peintner, Michael Müller, Eva Zangerle |
| 2024 | Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity Recommendations. Ine Coppens, Toon De Pessemier, Luc Martens |
| 2024 | Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation. David Eric Austin, Anton Korikov, Armin Toroghi, Scott Sanner |
| 2024 | Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations. Anima Singh, Trung Vu, Nikhil Mehta, Raghunandan H. Keshavan, Maheswaran Sathiamoorthy, Yilin Zheng, Lichan Hong, Lukasz Heldt, Li Wei, Devansh Tandon, Ed H. Chi, Xinyang Yi |
| 2024 | Bias in Book Recommendation. Savvina Daniil |
| 2024 | Biased User History Synthesis for Personalized Long-Tail Item Recommendation. Keshav Balasubramanian, Abdulla Alshabanah, Elan Markowitz, Greg Ver Steeg, Murali Annavaram |
| 2024 | Bootstrapping Conditional Retrieval for User-to-Item Recommendations. Hongtao Lin, Haoyu Chen, Jaewon Yang, Jiajing Xu |
| 2024 | Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other? Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco De Nadai, Hugues Bouchard |
| 2024 | Bridging Viewpoints in News with Recommender Systems. Jia Hua Jeng |
| 2024 | Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems. Nikhil Khani, Li Wei, Aniruddh Nath, Shawn Andrews, Shuo Yang, Yang Liu, Pendo Abbo, Maciej Kula, Jarrod Kahn, Zhe Zhao, Lichan Hong, Ed H. Chi |
| 2024 | CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation. Yaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulic, Anna Korhonen, Mohamed Hammad |
| 2024 | CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework. Francis Zac dela Cruz, Flora D. Salim, Yonchanok Khaokaew, Jeffrey Chan |
| 2024 | CEERS: Counterfactual Evaluations of Explanations in Recommender Systems. Mikhail Baklanov |
| 2024 | CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. Olivier Jeunen, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang |
| 2024 | Calibrating the Predictions for Top-N Recommendations. Masahiro Sato |
| 2024 | Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems. Elias Entrup, Ralph Ewerth, Anett Hoppe |
| 2024 | Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation System. Zhen Zhang, Qingyun Liu, Yuening Li, Sourabh Bansod, Mingyan Gao, Yaping Zhang, Zhe Zhao, Lichan Hong, Ed H. Chi, Shuchao Bi, Liang Liu |
| 2024 | CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation. Jieming Zhu, Mengqun Jin, Qijiong Liu, Zexuan Qiu, Zhenhua Dong, Xiu Li |
| 2024 | Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems. Yan-Martin Tamm, Anna Aljanaki |
| 2024 | Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and Healthcare. Bereket Abera Yilma |
| 2024 | ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning. Xiao Yu, Jinzhong Zhang, Zhou Yu |
| 2024 | Conducting Recommender Systems User Studies Using POPROX. Robin Burke, Joseph A. Konstan, Michael D. Ekstrand |
| 2024 | Conducting User Experiments in Recommender Systems. Bart P. Knijnenburg, Edward C. Malthouse |
| 2024 | Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data. Mahta Bakhshizadeh, Heiko Maus, Andreas Dengel |
| 2024 | Country-diverted experiments for mitigation of network effects. Lina Lin, Changping Meng, Jennifer Brennan, Jean Pouget-Abadie, Ningren Han, Shuchao Bi, Yajun Peng |
| 2024 | Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization. Abdulaziz Samra, Evgeny Frolov, Alexey Vasilev, Alexander Grigorevskiy, Anton Vakhrushev |
| 2024 | DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems. Sheng Zhang, Maolin Wang, Xiangyu Zhao, Ruocheng Guo, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Zijian Zhang, Hongzhi Yin |
| 2024 | Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation. Genki Kusano |
| 2024 | Deep Recommendation using Graphs. Panagiotis Symeonidis |
| 2024 | Democratizing Urban Mobility Through an Open-Source, Multi-Criteria Route Recommendation System. Alexander Eggerth, Javier Argota Sánchez-Vaquerizo, Dirk Helbing, Sachit Mahajan |
| 2024 | Discerning Canonical User Representation for Cross-Domain Recommendation. Siqian Zhao, Sherry Sahebi |
| 2024 | Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models. Yu Cui, Feng Liu, Pengbo Wang, Bohao Wang, Heng Tang, Yi Wan, Jun Wang, Jiawei Chen |
| 2024 | Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce. Yuan Wang, Zhiyu Li, Changshuo Zhang, Sirui Chen, Xiao Zhang, Jun Xu, Quan Lin |
| 2024 | Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data. Kristina Matrosova, Lilian Marey, Guillaume Salha-Galvan, Thomas Louail, Olivier Bodini, Manuel Moussallam |
| 2024 | Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations. Anton Klenitskiy, Anna Volodkevich, Anton Pembek, Alexey Vasilev |
| 2024 | Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce. Ádám Tibor Czapp, Mátyás Jani, Bálint Domián, Balázs Hidasi |
| 2024 | Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation. Weixin Li, Xiaolin Lin, Weike Pan, Zhong Ming |
| 2024 | EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language Models. Irene Li, Ruihai Dong, Lei Li, Li Chen |
| 2024 | Economics of Recommender Systems. Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello |
| 2024 | Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits. Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito |
| 2024 | Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items. Aleksandr Vladimirovich Petrov, Craig Macdonald, Nicola Tonellotto |
| 2024 | EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations. Chiyu Zhang, Yifei Sun, Minghao Wu, Jun Chen, Jie Lei, Muhammad Abdul-Mageed, Rong Jin, Angli Liu, Ji Zhu, Sem Park, Ning Yao, Bo Long |
| 2024 | Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark. Shijie Liu, Nan Zheng, Hui Kang, Xavier Simmons, Junjie Zhang, Matthias Langer, Wenjing Zhu, Minseok Lee, Zehuan Wang |
| 2024 | Embedding based retrieval for long tail search queries in ecommerce. Akshay Kekuda, Yuyang Zhang, Arun Udayashankar |
| 2024 | Encouraging Exploration in Spotify Search through Query Recommendations. Henrik Lindstrom, Humberto Jesús Corona Pampín, Enrico Palumbo, Alva Liu |
| 2024 | End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling. Zexu Sun, Hao Yang, Dugang Liu, Yunpeng Weng, Xing Tang, Xiuqiang He |
| 2024 | Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures. Thomas Elmar Kolb |
| 2024 | Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention. Rengan Xu, Junjie Yang, Yifan Xu, Hong Li, Xing Liu, Devashish Shankar, Haoci Zhang, Meng Liu, Boyang Li, Yuxi Hu, Mingwei Tang, Zehua Zhang, Tunhou Zhang, Dai Li, Sijia Chen, Gian-Paolo Musumeci, Jiaqi Zhai, Bill Zhu, Hong Yan, Srihari Reddy |
| 2024 | Enhancing Privacy in Recommender Systems through Differential Privacy Techniques. Angela Di Fazio |
| 2024 | Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias. Venkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grün |
| 2024 | Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning. Pavan Seshadri, Shahrzad Shashaani, Peter Knees |
| 2024 | Enhancing Sequential Music Recommendation with Personalized Popularity Awareness. Davide Abbattista, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald, Aleksandr Vladimirovich Petrov |
| 2024 | Entity-Aware Collections Ranking: A Joint Scoring Approach. Sihao Chen, Sheng Li, Youhe Chen, Dong Yang |
| 2024 | Evaluating the Pros and Cons of Recommender Systems Explanations. Kathrin Wardatzky |
| 2024 | Evaluation and simplification of text difficulty using LLMs in the context of recommending texts in French to facilitate language learning. Henri Jamet, Maxime Manderlier, Yash Raj Shrestha, Michalis Vlachos |
| 2024 | Explainability in Music Recommender System. Shahrzad Shashaani |
| 2024 | Explainable Multi-Stakeholder Job Recommender Systems. Roan Schellingerhout |
| 2024 | Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge Graphs. Neda Afreen |
| 2024 | Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities. Linus W. Dietz, Sanja Scepanovic, Ke Zhou, Daniele Quercia |
| 2024 | Explore versus repeat: insights from an online supermarket. Mariagiorgia Agnese Tandoi, Daniela Solis Morales |
| 2024 | Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems. Zheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor |
| 2024 | FAccTRec 2024: The 7th Workshop on Responsible Recommendation. Michael D. Ekstrand, Toshihiro Kamishima, Amifa Raj, Karlijn Dinnissen |
| 2024 | FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction. Hangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen, Chenxu Zhu, Ruiming Tang, Weinan Zhang, Yong Yu |
| 2024 | Fair Augmentation for Graph Collaborative Filtering. Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda |
| 2024 | Fair Reciprocal Recommendation in Matching Markets. Yoji Tomita, Tomohiko Yokoyama |
| 2024 | FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems. Qin Liu, Xuan Feng, Tianlong Gu, Xiaoli Liu |
| 2024 | Fairness Explanations in Recommender Systems. Luan Soares de Souza |
| 2024 | Fairness Matters: A look at LLM-generated group recommendations. Antonela Tommasel |
| 2024 | Fairness and Transparency in Music Recommender Systems: Improvements for Artists. Karlijn Dinnissen |
| 2024 | FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations. Yuchen Ding, Siqing Zhang, Boyu Fan, Wei Sun, Yong Liao, Peng Yuan Zhou |
| 2024 | First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024). Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesco Ricci |
| 2024 | Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024). Toine Bogers, David Graus, Mesut Kaya, Chris Johnson, Jens-Joris Decorte, Tijl De Bie |
| 2024 | From Clicks to Carbon: The Environmental Toll of Recommender Systems. Tobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel |
| 2024 | GLAMOR: Graph-based LAnguage MOdel embedding for citation Recommendation. Zafar Ali, Guilin Qi, Irfan Ullah, Adam A. Q. Mohammed, Pavlos Kefalas, Khan Muhammad |
| 2024 | GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMs. Ulysse Maes, Lien Michiels, Annelien Smets |
| 2024 | How to Evaluate Serendipity in Recommender Systems: the Need for a Serendiptionnaire. Brett Binst |
| 2024 | Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial Training. Jingyu Chen, Lilin Zhang, Ning Yang |
| 2024 | Improving Data Efficiency for Recommenders and LLMs. Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, James Caverlee, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng |
| 2024 | Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System. Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng |
| 2024 | Information-Controllable Graph Contrastive Learning for Recommendation. Zirui Guo, Yanhua Yu, Yuling Wang, Kangkang Lu, Zixuan Yang, Liang Pang, Tat-Seng Chua |
| 2024 | Informed Dataset Selection with 'Algorithm Performance Spaces'. Joeran Beel, Lukas Wegmeth, Lien Michiels, Steffen Schulz |
| 2024 | Informfully - Research Platform for Reproducible User Studies. Lucien Heitz, Julian Andrea Croci, Madhav Sachdeva, Abraham Bernstein |
| 2024 | Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations. Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro |
| 2024 | Integrating Matrix Factorization with Graph based Models. Rachana Mehta |
| 2024 | Is It Really Complementary? Revisiting Behavior-based Labels for Complementary Recommendation. Kai Sugahara, Chihiro Yamasaki, Kazushi Okamoto |
| 2024 | It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender Metrics. Hanne Vandenbroucke, Annelien Smets |
| 2024 | It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. Andres Ferraro, Michael D. Ekstrand, Christine Bauer |
| 2024 | Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn). Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede |
| 2024 | KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation. Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras, Alessandro Soccol |
| 2024 | Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective Recommendation. Zeyuan Meng, Zixuan Yi, Iadh Ounis |
| 2024 | LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding. Zhizhong Wan, Bin Yin, Junjie Xie, Fei Jiang, Xiang Li, Wei Lin |
| 2024 | LLMs for User Interest Exploration in Large-scale Recommendation Systems. Jianling Wang, Haokai Lu, Yifan Liu, He Ma, Yueqi Wang, Yang Gu, Shuzhou Zhang, Ningren Han, Shuchao Bi, Lexi Baugher, Ed H. Chi, Minmin Chen |
| 2024 | Large Language Models as Evaluators for Recommendation Explanations. Xiaoyu Zhang, Yishan Li, Jiayin Wang, Bowen Sun, Weizhi Ma, Peijie Sun, Min Zhang |
| 2024 | Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction. Yi Wu, Daryl Chang, Jennifer She, Zhe Zhao, Li Wei, Lukasz Heldt |
| 2024 | Learning Personalized Health Recommendations via Offline Reinforcement Learning. Larry Donald Preuett |
| 2024 | Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems. Thi Ngoc Trang Tran, Seda Polat Erdeniz, Alexander Felfernig, Sebastian Lubos, Merfat El Mansi, Viet-Man Le |
| 2024 | Leveraging LLM generated labels to reduce bad matches in job recommendations. Yingchi Pei, Yi Wei Pang, Warren Cai, Nilanjan Sengupta, Dheeraj Toshniwal |
| 2024 | Leveraging Monte Carlo Tree Search for Group Recommendation. Antonela Tommasel, J. Andres Diaz-Pace |
| 2024 | Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation. Alex Shtoff, Michael Viderman, Naama Haramaty-Krasne, Oren Somekh, Ariel Raviv, Tularam Ban |
| 2024 | LyricLure: Mining Catchy Hooks in Song Lyrics to Enhance Music Discovery and Recommendation. Siddharth Sharma, Akshay Shukla, Ajinkya Walimbe, Tarun Sharma, Joaquin Delgado |
| 2024 | MARec: Metadata Alignment for cold-start Recommendation. Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder, Anton van den Hengel |
| 2024 | MAWI Rec: Leveraging Severe Weather Data in Recommendation. Brendan Andrew Duncan, Surya Kallumadi, Taylor Berg-Kirkpatrick, Julian J. McAuley |
| 2024 | MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. Zhiming Yang, Haining Gao, Dehong Gao, Luwei Yang, Libin Yang, Xiaoyan Cai, Wei Ning, Guannan Zhang |
| 2024 | MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations. Yuezihan Jiang, Changyu Li, Gaode Chen, Peiyi Li, Qi Zhang, Jingjian Lin, Peng Jiang, Fei Sun, Wentao Zhang |
| 2024 | MODEM: Decoupling User Behavior for Shared-Account Video Recommendations on Large Screen Devices. Jiang Li, Zhen Zhang, Xiang Feng, Muyang Li, Yongqi Liu, Lantao Hu |
| 2024 | MuRS 2024: 2nd Music Recommender Systems Workshop. Andres Ferraro, Lorenzo Porcaro, Peter Knees, Christine Bauer |
| 2024 | Multi-Behavioral Sequential Recommendation. Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme |
| 2024 | Multi-Objective Recommendation via Multivariate Policy Learning. Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko |
| 2024 | Multi-Preview Recommendation via Reinforcement Learning. Yang Xu, Kuan-Ting Lai, Pengcheng Xiong, Zhong Wu |
| 2024 | Multimodal Representation Learning for High-Quality Recommendations in Cold-Start and Beyond-Accuracy. Marta Moscati |
| 2024 | NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems. Alain Starke, Sanne Vrijenhoek, Lien Michiels, Johannes Kruse, Nava Tintarev |
| 2024 | Neighborhood-Based Collaborative Filtering for Conversational Recommendation. Zhouhang Xie, Junda Wu, Hyunsik Jeon, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, Julian J. McAuley |
| 2024 | Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias. Lulu Dong, Guoxiu He, Aixin Sun |
| 2024 | Off-Policy Selection for Optimizing Ad Display Timing in Mobile Games (Samsung Instant Plays). Katarzyna Siudek-Tkaczuk, Slawomir Kapka, Jedrzej Alchimowicz, Bartlomiej Swoboda, Michal Romaniuk |
| 2024 | Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl |
| 2024 | On Interpretability of Linear Autoencoders. Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska |
| 2024 | One-class Matrix Factorization: Point-Wise Regression-Based or Pair-Wise Ranking-Based? Sheng-Wei Chen, Chih-Jen Lin |
| 2024 | One-class recommendation systems with the hinge pairwise distance loss and orthogonal representations. Ramin Raziperchikolaei, Young-joo Chung |
| 2024 | Optimal Baseline Corrections for Off-Policy Contextual Bandits. Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke |
| 2024 | Optimizing for Participation in Recommender System. Yuan Shao, Bibang Liu, Sourabh Bansod, Arnab Bhadury, Mingyan Gao, Yaping Zhang |
| 2024 | Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems. Timo Wilm, Philipp Normann, Felix Stepprath |
| 2024 | Pay Attention to Attention for Sequential Recommendation. Yuli Liu, Min Liu, Xiaojing Liu |
| 2024 | Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User Engagement. Bianca Maria Deconcini |
| 2024 | Playlist Search Reinvented: LLMs Behind the Curtain. Geetha Sai Aluri, Siddharth Sharma, Tarun Sharma, Joaquin Delgado |
| 2024 | Positive-Sum Impact of Multistakeholder Recommender Systems for Urban Tourism Promotion and User Utility. Pavel Merinov, Francesco Ricci |
| 2024 | Powerful A/B-Testing Metrics and Where to Find Them. Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko |
| 2024 | Privacy Preserving Conversion Modeling in Data Clean Room. Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu, Jiankai Sun, Behnam Rezaei |
| 2024 | Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024 Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, Ben London |
| 2024 | Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation. Lanling Xu, Zihan Lin, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, Ji-Rong Wen |
| 2024 | Prompt Tuning for Item Cold-start Recommendation. Yuezihan Jiang, Gaode Chen, Wenhan Zhang, Jingchi Wang, Yinjie Jiang, Qi Zhang, Jingjian Lin, Peng Jiang, Kaigui Bian |
| 2024 | Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders. Robin Ungruh, Karlijn Dinnissen, Anja Volk, Maria Soledad Pera, Hanna Hauptmann |
| 2024 | RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems. Shuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai |
| 2024 | Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending. Jan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini |
| 2024 | Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space. Yu Liu, Qinglin Jia, Shuting Shi, Chuhan Wu, Zhaocheng Du, Zheng Xie, Ruiming Tang, Muyu Zhang, Ming Li |
| 2024 | ReChorus2.0: A Modular and Task-Flexible Recommendation Library. Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang, Shaoping Ma |
| 2024 | ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool. Changxin Tian, Binbin Hu, Chunjing Gan, Haoyu Chen, Zhuo Zhang, Li Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Jiawei Chen |
| 2024 | RePlay: a Recommendation Framework for Experimentation and Production Use. Alexey Vasilev, Anna Volodkevich, Denis Kulandin, Tatiana Bysheva, Anton Klenitskiy |
| 2024 | RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen |
| 2024 | RecTemp: Temporal Reasoning in Recommendation Systems. Adir Solomon, Tsvi Kuflik, Bracha Shapira, Ido Guy |
| 2024 | Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets. Lukas Wegmeth, Tobias Vente, Joeran Beel |
| 2024 | Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models. Alessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro |
| 2024 | Recommending Personalised Targeted Training Adjustments for Marathon Runners. Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth |
| 2024 | Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES). Alan Said, Christine Bauer, Eva Zangerle |
| 2024 | Repeated Padding for Sequential Recommendation. Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao |
| 2024 | Reproducibility and Analysis of Scientific Dataset Recommendation Methods. Ornella Irrera, Matteo Lissandrini, Daniele Dell'Aglio, Gianmaria Silvello |
| 2024 | Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm. Pasquale Lops, Antonio Silletti, Marco Polignano, Cataldo Musto, Giovanni Semeraro |
| 2024 | Revisiting BPR: A Replicability Study of a Common Recommender System Baseline. Aleksandr Milogradskii, Oleg Lashinin, Alexander P, Marina Ananyeva, Sergey Kolesnikov |
| 2024 | Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation. Geon Lee, Kyungho Kim, Kijung Shin |
| 2024 | Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery. Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang |
| 2024 | RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems. Valerio Guarrasi, Federico Siciliano, Fabrizio Silvestri |
| 2024 | Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues. Tri Kurniawan Wijaya, Edoardo D'Amico, Gábor Fodor, Manuel V. Loureiro |
| 2024 | SURE 2024: Workshop on Strategic and Utility-aware REcommendation. Himan Abdollahpouri, Tonia Danylenko, Masoud Mansoury, Babak Loni, Daniel Russo, Mihajlo Grbovic |
| 2024 | Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs. Gleb Mezentsev, Danil Gusak, Ivan V. Oseledets, Evgeny Frolov |
| 2024 | Scale-Invariant Learning-to-Rank. Alessio Petrozziello, Christian Sommeregger, Ye-Sheen Lim |
| 2024 | Scaling Law of Large Sequential Recommendation Models. Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen |
| 2024 | Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction. Wenhao Li, Jie Zhou, Chuan Luo, Chao Tang, Kun Zhang, Shixiong Zhao |
| 2024 | SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest Recommendations. Shirui Wang, Bohan Xie, Ling Ding, Xiaoying Gao, Jianting Chen, Yang Xiang |
| 2024 | Self-Attentive Sequential Recommendations with Hyperbolic Representations. Evgeny Frolov, Tatyana Matveeva, Leyla Mirvakhabova, Ivan V. Oseledets |
| 2024 | Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders. Yin Zhang, Ruoxi Wang, Xiang Li, Tiansheng Yao, Andrew Evdokimov, Jonathan Valverde, Yuan Gao, Jerry Zhang, Evan Ettinger, Ed H. Chi, Derek Zhiyuan Cheng |
| 2024 | Short-form Video Needs Long-term Interests: An Industrial Solution for Serving Large User Sequence Models. Yuening Li, Diego Uribe, Chuan He, Jiaxi Tang, Qingyun Liu, Junjie Shan, Ben Most, Kaushik Kalyan, Shuchao Bi, Xinyang Yi, Lichan Hong, Ed H. Chi, Liang Liu |
| 2024 | Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). Vito Walter Anelli, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker |
| 2024 | Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models. Swanand Joshi, Yesu Feng, Ko-Jen Hsiao, Zhe Zhang, Sudarshan Lamkhede |
| 2024 | Social Choice for Heterogeneous Fairness in Recommendation. Amanda Aird, Elena Stefancova, Cassidy All, Amy Voida, Martin Homola, Nicholas Mattei, Robin Burke |
| 2024 | Societal Sorting as a Systemic Risk of Recommenders. Luke Thorburn, Maria Polukarov, Carmine Ventre |
| 2024 | Stalactite: toolbox for fast prototyping of vertical federated learning systems. Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy |
| 2024 | Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender Systems. Mahta Bakhshizadeh |
| 2024 | TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation Tasks. Jiaye Lin, Shuang Peng, Zhong Zhang, Peilin Zhao |
| 2024 | Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training. Yi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai, Jiajing Xu |
| 2024 | The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN). Yashar Deldjoo, Julian J. McAuley, Scott Sanner, Pablo Castells, Shuai Zhang, Enrico Palumbo |
| 2024 | The 6th International Workshop on Health Recommender Systems. Hanna Hauptmann, Christoph Trattner, Helma Torkamaan |
| 2024 | The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation. Zekai Qu, Ruobing Xie, Chaojun Xiao, Zhanhui Kang, Xingwu Sun |
| 2024 | The Fault in Our Recommendations: On the Perils of Optimizing the Measurable. Omar Besbes, Yash Kanoria, Akshit Kumar |
| 2024 | The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems. Guy Aridor, Duarte Gonçalves, Ruoyan Kong, Daniel Kluver, Joseph A. Konstan |
| 2024 | The Role of Unknown Interactions in Implicit Matrix Factorization - A Probabilistic View. Joey De Pauw, Bart Goethals |
| 2024 | Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation. Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu, Xiuqiang He |
| 2024 | Toward 100TB Recommendation Models with Embedding Offloading. Intaik Park, Ehsan K. Ardestani, Damian Reeves, Sarunya Pumma, Henry Tsang, Levy Zhao, Jian He, Joshua Deng, Dennis van der Staay, Yu Guo, Paul Zhang |
| 2024 | Towards Empathetic Conversational Recommender Systems. Xiaoyu Zhang, Ruobing Xie, Yougang Lyu, Xin Xin, Pengjie Ren, Mingfei Liang, Bo Zhang, Zhanhui Kang, Maarten de Rijke, Zhaochun Ren |
| 2024 | Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances. Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro |
| 2024 | Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu |
| 2024 | Towards Sustainable Recommendations in Urban Tourism. Pavel Merinov |
| 2024 | Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems. Alessandro Petruzzelli |
| 2024 | Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie Recommendations. Bora Edizel, Tim Sweetser, Ashok Chandrashekar, Kamilia Ahmadi, Puja Das |
| 2024 | Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation. Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra, Romain Hennequin |
| 2024 | Understanding Fairness in Recommender Systems: A Healthcare Perspective. Veronica Kecki, Alan Said |
| 2024 | Unified Denoising Training for Recommendation. Haoyan Chua, Yingpeng Du, Zhu Sun, Ziyan Wang, Jie Zhang, Yew-Soon Ong |
| 2024 | Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender Systems. Ting Yang, Li Chen |
| 2024 | Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data. Yuhan Zhao, Rui Chen, Qilong Han, Hongtao Song, Li Chen |
| 2024 | User Knowledge Prompt for Sequential Recommendation. Yuuki Tachioka |
| 2024 | Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction. Jiahui Huang, Lan Zhang, Junhao Wang, Shanyang Jiang, Dongbo Huang, Cheng Ding, Lan Xu |
| 2024 | VideoRecSys + LargeRecSys 2024. Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Saurabh Gupta, Brad Schumitsch, Arnab Bhadury, Ding Tong, Ko-Jen Hsiao, Liang Liu |
| 2024 | What to compare? Towards understanding user sessions on price comparison platforms. Ahmadou Wagne, Julia Neidhardt |
| 2024 | Why the Shooting in the Dark Method Dominates Recommender Systems Practice. David Rohde |
| 2024 | Workshop on Context-Aware Recommender Systems (CARS) 2024. Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger |
| 2024 | Workshop on Recommenders in Tourism (RecTour) 2024. Julia Neidhardt, Tsvi Kuflik, Amit Livne, Markus Zanker |
| 2024 | beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems. Vojtech Vancura, Pavel Kordík, Milan Straka |
| 2024 | Δ-OPE: Off-Policy Estimation with Pairs of Policies. Olivier Jeunen, Aleksei Ustimenko |