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ORCIDRecSys 2024: Bari, Italy
- Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, Ben London:
Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024. ACM 2024, ISBN 979-8-4007-0505-2
Large language models
- Shirui Wang
, Bohan Xie, Ling Ding, Xiaoying Gao, Jianting Chen, Yang Xiang:
SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest Recommendations. 1-11
Short papers
- Guy Aridor, Duarte Gonçalves, Ruoyan Kong, Daniel Kluver, Joseph A. Konstan:
The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems. 1
Large language models
- Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu:
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. 12-22 - Zhizhong Wan, Bin Yin, Junjie Xie, Fei Jiang, Xiang Li, Wei Lin:
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding. 23-32 - Xiaoyu Zhang, Yishan Li, Jiayin Wang, Bowen Sun, Weizhi Ma, Peijie Sun, Min Zhang:
Large Language Models as Evaluators for Recommendation Explanations. 33-42 - Ting Yang, Li Chen:
Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender Systems. 43-52 - Zekai Qu, Ruobing Xie, Chaojun Xiao, Zhanhui Kang, Xingwu Sun:
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation. 53-62 - Changxin Tian, Binbin Hu, Chunjing Gan, Haoyu Chen, Zhuo Zhang, Li Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Jiawei Chen:
ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool. 63-73 - David Eric Austin, Anton Korikov, Armin Toroghi, Scott Sanner:
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation. 74-83 - Xiaoyu Zhang, Ruobing Xie, Yougang Lyu, Xin Xin, Pengjie Ren, Mingfei Liang, Bo Zhang, Zhanhui Kang, Maarten de Rijke, Zhaochun Ren:
Towards Empathetic Conversational Recommender Systems. 84-93 - Hangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen, Chenxu Zhu, Ruiming Tang, Weinan Zhang, Yong Yu:
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction. 94-104 - Alejandro Ariza-Casabona, Ludovico Boratto, Maria Salamó:
A Comparative Analysis of Text-Based Explainable Recommender Systems. 105-115 - Pasquale Lops, Antonio Silletti, Marco Polignano, Cataldo Musto, Giovanni Semeraro:
Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm. 116-125
Bias and fairness
- Qin Liu, Xuan Feng, Tianlong Gu, Xiaoli Liu:
FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems. 126-136 - Elizabeth Gómez, David Contreras, Ludovico Boratto, Maria Salamó:
AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders. 137-147 - Kristina Matrosova, Lilian Marey, Guillaume Salha-Galvan, Thomas Louail, Olivier Bodini, Manuel Moussallam:
Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data. 148-157 - Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda:
Fair Augmentation for Graph Collaborative Filtering. 158-168 - Robin Ungruh, Karlijn Dinnissen, Anja Volk, Maria Soledad Pera, Hanna Hauptmann:
Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders. 169-178 - Lulu Dong, Guoxiu He, Aixin Sun:
Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias. 179-188 - Keshav Balasubramanian, Abdulla Alshabanah, Elan Markowitz, Greg Ver Steeg, Murali Annavaram:
Biased User History Synthesis for Personalized Long-Tail Item Recommendation. 189-199 - Omar Besbes, Yash Kanoria, Akshit Kumar:
The Fault in Our Recommendations: On the Perils of Optimizing the Measurable. 200-208 - Yoji Tomita, Tomohiko Yokoyama:
Fair Reciprocal Recommendation in Matching Markets. 209-218
Collaborative filtering
- Joey De Pauw, Bart Goethals:
The Role of Unknown Interactions in Implicit Matrix Factorization - A Probabilistic View. 219-227 - Mengduo Yang, Yi Yuan, Jie Zhou, Meng Xi, Xiaohua Pan, Ying Li, Yangyang Wu, Jinshan Zhang, Jianwei Yin:
Adaptive Fusion of Multi-View for Graph Contrastive Recommendation. 228-237 - Alex Shtoff, Michael Viderman, Naama Haramaty-Krasne, Oren Somekh, Ariel Raviv, Tularam Ban:
Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation. 238-246 - Yuhan Zhao, Rui Chen, Qilong Han, Hongtao Song, Li Chen:
Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data. 247-256 - Sheng-Wei Chen, Chih-Jen Lin:
One-class Matrix Factorization: Point-Wise Regression-Based or Pair-Wise Ranking-Based? 257-266 - Aleksandr Milogradskii, Oleg Lashinin, Alexander P, Marina Ananyeva, Sergey Kolesnikov:
Revisiting BPR: A Replicability Study of a Common Recommender System Baseline. 267-277
Cross-domain and cross-modal learning
- Jingyu Chen, Lilin Zhang, Ning Yang:
Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial Training. 278-286 - Zhiming Yang, Haining Gao, Dehong Gao, Luwei Yang, Libin Yang, Xiaoyan Cai, Wei Ning, Guannan Zhang:
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. 287-297 - Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations. 298-308 - Abdulaziz Samra, Evgeny Frolov, Alexey Vasilev, Alexander Grigorevskiy, Anton Vakhrushev:
Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization. 309-317 - Siqian Zhao, Sherry Sahebi:
Discerning Canonical User Representation for Cross-Domain Recommendation. 318-328
Multi-task learning
- Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu, Xiuqiang He:
Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation. 329-339 - Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco De Nadai, Hugues Bouchard:
Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other? 340-349 - Jiahui Huang, Lan Zhang, Junhao Wang, Shanyang Jiang, Dongbo Huang, Cheng Ding, Lan Xu:
Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction. 350-359 - Yu Liu, Qinglin Jia, Shuting Shi, Chuhan Wu, Zhaocheng Du, Zheng Xie, Ruiming Tang, Muyu Zhang, Ming Li:
Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space. 360-369
Cold-start
- Wenhao Li, Jie Zhou, Chuan Luo, Chao Tang, Kun Zhang, Shixiong Zhao:
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction. 370-379 - Christian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, Markus Schedl:
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios. 380-390 - Gaode Chen, Ruina Sun, Yuezihan Jiang, Jiangxia Cao, Qi Zhang, Jingjian Lin, Han Li, Kun Gai, Xinghua Zhang:
A Multi-modal Modeling Framework for Cold-start Short-video Recommendation. 391-400 - Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder, Anton van den Hengel:
MARec: Metadata Alignment for cold-start Recommendation. 401-410 - Yuezihan Jiang, Gaode Chen, Wenhan Zhang, Jingchi Wang, Yinjie Jiang, Qi Zhang, Jingjian Lin, Peng Jiang, Kaigui Bian:
Prompt Tuning for Item Cold-start Recommendation. 411-421
Sequential recommendation
- Yaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulic, Anna Korhonen, Mohamed Hammad:
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation. 422-432 - Junting Wang, Praneet Rathi, Hari Sundaram:
A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics. 433-443 - Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen:
Scaling Law of Large Sequential Recommendation Models. 444-453 - Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang, Shaoping Ma:
ReChorus2.0: A Modular and Task-Flexible Recommendation Library. 454-464 - Weixin Li, Xiaolin Lin, Weike Pan, Zhong Ming:
Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation. 465-474 - Gleb Mezentsev, Danil Gusak, Ivan V. Oseledets, Evgeny Frolov:
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs. 475-485 - Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra, Romain Hennequin:
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation. 486-496 - Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao:
Repeated Padding for Sequential Recommendation. 497-506 - Yu Cui, Feng Liu, Pengbo Wang, Bohao Wang, Heng Tang, Yi Wan, Jun Wang, Jiawei Chen:
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models. 507-517
Graph learning
- Zixuan Yi, Iadh Ounis:
A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation. 518-527 - Zirui Guo, Yanhua Yu, Yuling Wang, Kangkang Lu, Zixuan Yang, Liang Pang, Tat-Seng Chua:
Information-Controllable Graph Contrastive Learning for Recommendation. 528-537 - Yuezihan Jiang, Changyu Li, Gaode Chen, Peiyi Li, Qi Zhang, Jingjian Lin, Peng Jiang, Fei Sun, Wentao Zhang:
MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations. 538-548 - Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio:
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. 549-559
Optimisation and evaluation
- Zexu Sun, Hao Yang, Dugang Liu, Yunpeng Weng, Xing Tang, Xiuqiang He:
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling. 560-569 - Ornella Irrera, Matteo Lissandrini, Daniele Dell'Aglio, Gianmaria Silvello:
Reproducibility and Analysis of Scientific Dataset Recommendation Methods. 570-579 - Tobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel:
From Clicks to Carbon: The Environmental Toll of Recommender Systems. 580-590 - Sheng Zhang, Maolin Wang, Xiangyu Zhao, Ruocheng Guo, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Zijian Zhang, Hongzhi Yin:
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems. 591-600 - Xiao Yu, Jinzhong Zhang, Zhou Yu:
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning. 601-611 - Haoyan Chua, Yingpeng Du, Zhu Sun, Ziyan Wang, Jie Zhang, Yew-Soon Ong:
Unified Denoising Training for Recommendation. 612-621 - Shijie Liu, Nan Zheng, Hui Kang, Xavier Simmons, Junjie Zhang, Matthias Langer, Wenjing Zhu, Minseok Lee, Zehuan Wang:
Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark. 622-632 - Yang Yang, Bo Chen, Chenxu Zhu, Menghui Zhu, Xinyi Dai, Huifeng Guo, Muyu Zhang, Zhenhua Dong, Ruiming Tang:
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising. 633-642 - Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang:
Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery. 643-653 - Mahta Bakhshizadeh, Heiko Maus, Andreas Dengel:
Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data. 654-659 - Lucien Heitz, Julian Andrea Croci, Madhav Sachdeva, Abraham Bernstein:
Informfully - Research Platform for Reproducible User Studies. 660-669
Robust recommender systems
- Shuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai:
RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems. 670-679 - Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng:
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System. 680-689 - Yuchen Ding, Siqing Zhang, Boyu Fan, Wei Sun, Yong Liao, Peng Yuan Zhou:
FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations. 690-700 - Yunfan Wu, Qi Cao, Shuchang Tao, Kaike Zhang, Fei Sun, Huawei Shen:
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems. 701-711
Off-policy learning
- Olivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko:
Multi-Objective Recommendation via Multivariate Policy Learning. 712-721 - Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke:
Optimal Baseline Corrections for Off-Policy Contextual Bandits. 722-732 - Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito:
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits. 733-741
Industry track
- Franklin Horn, Aurelia Alston, Won J. You:
"More to Read" at the Los Angeles Times: Solving a Cold Start Problem with LLMs to Improve Story Discovery. 742-744 - Guangtao Nie, Rong Zhi, Xiaofan Yan, Yufan Du, Xiangyang Zhang, Jianwei Chen, Mi Zhou, Hongshen Chen, Tianhao Li, Ziguang Cheng, Sulong Xu, Jinghe Hu:
A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce. 745-747 - Jan Hartman, Hitesh Sagtani, Julie Tibshirani, Rishabh Mehrotra:
AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations. 748-750 - Jaidev Shah, Gang Luo, Jialin Liu, Amey Barapatre, Fan Wu, Chuck Wang, Hongzhi Li:
Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models. 751-754 - Hongtao Lin, Haoyu Chen, Jaewon Yang, Jiajing Xu:
Bootstrapping Conditional Retrieval for User-to-Item Recommendations. 755-757 - 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:
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems. 758-761 - Zhen Zhang, Qingyun Liu, Yuening Li, Sourabh Bansod, Mingyan Gao, Yaping Zhang, Zhe Zhao, Lichan Hong, Ed H. Chi, Shuchao Bi, Liang Liu:
Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation System. 762-764 - Lina Lin, Changping Meng, Jennifer Brennan, Jean Pouget-Abadie, Ningren Han, Shuchao Bi, Yajun Peng:
Country-diverted experiments for mitigation of network effects. 765-767 - Ádám Tibor Czapp, Mátyás Jani, Bálint Domián, Balázs Hidasi:
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce. 768-770 - Akshay Kekuda, Yuyang Zhang, Arun Udayashankar:
Embedding based retrieval for long tail search queries in ecommerce. 771-774 - Henrik Lindstrom, Humberto Jesús Corona Pampín, Enrico Palumbo, Alva Liu:
Encouraging Exploration in Spotify Search through Query Recommendations. 775-777 - 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:
Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention. 778-780 - Venkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grün:
Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias. 781-783 - Sihao Chen, Sheng Li, Youhe Chen, Dong Yang:
Entity-Aware Collections Ranking: A Joint Scoring Approach. 784-786 - Mariagiorgia Agnese Tandoi, Daniela Solis Morales:
Explore versus repeat: insights from an online supermarket. 787-789 - Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, James Caverlee, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng:
Improving Data Efficiency for Recommenders and LLMs. 790-792 - Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede:
Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn). 793-795 - Yingchi Pei, Yi Wei Pang, Warren Cai, Nilanjan Sengupta, Dheeraj Toshniwal:
Leveraging LLM generated labels to reduce bad matches in job recommendations. 796-799 - Siddharth Sharma, Akshay Shukla, Ajinkya Walimbe, Tarun Sharma, Joaquin Delgado:
LyricLure: Mining Catchy Hooks in Song Lyrics to Enhance Music Discovery and Recommendation. 800-802 - Katarzyna Siudek-Tkaczuk, Slawomir Kapka, Jedrzej Alchimowicz, Bartlomiej Swoboda, Michal Romaniuk:
Off-Policy Selection for Optimizing Ad Display Timing in Mobile Games (Samsung Instant Plays). 803-805 - Yuan Shao, Bibang Liu, Sourabh Bansod, Arnab Bhadury, Mingyan Gao, Yaping Zhang:
Optimizing for Participation in Recommender System. 806-808 - Timo Wilm, Philipp Normann, Felix Stepprath:
Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems. 809-812 - Geetha Sai Aluri, Siddharth Sharma, Tarun Sharma, Joaquin Delgado:
Playlist Search Reinvented: LLMs Behind the Curtain. 813-815 - Olivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko:
Powerful A/B-Testing Metrics and Where to Find Them. 816-818 - Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu, Jiankai Sun, Behnam Rezaei:
Privacy Preserving Conversion Modeling in Data Clean Room. 819-822 - Jan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini:
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending. 823-825 - Alessio Petrozziello, Christian Sommeregger, Ye-Sheen Lim:
Scale-Invariant Learning-to-Rank. 826-828 - Yin Zhang, Ruoxi Wang, Xiang Li, Tiansheng Yao, Andrew Evdokimov, Jonathan Valverde, Yuan Gao, Jerry Zhang, Evan Ettinger, Ed H. Chi, Derek Zhiyuan Cheng:
Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders. 829-831 - 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:
Short-form Video Needs Long-term Interests: An Industrial Solution for Serving Large User Sequence Models. 832-834 - Swanand Joshi, Yesu Feng, Ko-Jen Hsiao, Zhe Zhang, Sudarshan Lamkhede:
Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models. 835-837 - Yi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai, Jiajing Xu:
Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training. 838-840 - 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:
Toward 100TB Recommendation Models with Embedding Offloading. 841-843 - Bora Edizel, Tim Sweetser, Ashok Chandrashekar, Kamilia Ahmadi, Puja Das:
Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie Recommendations. 844-846 - David Rohde:
Why the Shooting in the Dark Method Dominates Recommender Systems Practice. 847-849
Short papers
- Brendan Andrew Duncan, Surya Kallumadi, Taylor Berg-Kirkpatrick, Julian J. McAuley:
MAWI Rec: Leveraging Severe Weather Data in Recommendation. 850-854 - Genki Kusano:
Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation. 861-865 - Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro:
Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances. 866-871 - 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:
LLMs for User Interest Exploration in Large-scale Recommendation Systems. 872-877 - Olivier Jeunen, Aleksei Ustimenko:
Δ-OPE: Off-Policy Estimation with Pairs of Policies. 878-883 - Andres Ferraro, Michael D. Ekstrand, Christine Bauer:
It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. 884-889 - Yuli Liu, Min Liu, Xiaojing Liu:
Pay Attention to Attention for Sequential Recommendation. 890-895 - Yuan Wang, Zhiyu Li, Changshuo Zhang, Sirui Chen, Xiao Zhang, Jun Xu, Quan Lin:
Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce. 896-901 - Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme:
Multi-Behavioral Sequential Recommendation. 902-906 - Jiang Li, Zhen Zhang, Xiang Feng, Muyang Li, Yongqi Liu, Lantao Hu:
MODEM: Decoupling User Behavior for Shared-Account Video Recommendations on Large Screen Devices. 907-911 - Aleksandr Vladimirovich Petrov, Craig Macdonald, Nicola Tonellotto:
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items. 912-917 - Lanling Xu, Zihan Lin, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, Ji-Rong Wen:
Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation. 918-923 - Francis Zac dela Cruz, Flora D. Salim, Yonchanok Khaokaew, Jeffrey Chan:
CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework. 924-928 - Zafar Ali, Guilin Qi, Irfan Ullah, Adam A. Q. Mohammed, Pavlos Kefalas, Khan Muhammad:
GLAMOR: Graph-based LAnguage MOdel embedding for citation Recommendation. 929-933 - Yan-Martin Tamm, Anna Aljanaki:
Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems. 934-938 - Pavel Merinov, Francesco Ricci:
Positive-Sum Impact of Multistakeholder Recommender Systems for Urban Tourism Promotion and User Utility. 939-944 - Karl Audun Kagnes Borgersen, Morten Goodwin, Morten Grundetjern, Jivitesh Sharma:
A Dataset for Adapting Recommender Systems to the Fashion Rental Economy. 945-950 - Luke Thorburn, Maria Polukarov, Carmine Ventre:
Societal Sorting as a Systemic Risk of Recommenders. 951-956 - Geon Lee, Kyungho Kim, Kijung Shin:
Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation. 957-962 - Masahiro Sato:
Calibrating the Predictions for Top-N Recommendations. 963-968 - Jieming Zhu, Mengqun Jin, Qijiong Liu, Zexuan Qiu, Zhenhua Dong, Xiu Li:
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation. 969-974 - Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska:
On Interpretability of Linear Autoencoders. 975-980 - Evgeny Frolov, Tatyana Matveeva, Leyla Mirvakhabova, Ivan V. Oseledets:
Self-Attentive Sequential Recommendations with Hyperbolic Representations. 981-986 - Henri Jamet, Maxime Manderlier, Yash Raj Shrestha, Michalis Vlachos:
Evaluation and simplification of text difficulty using LLMs in the context of recommending texts in French to facilitate language learning. 987-992 - Antonela Tommasel:
Fairness Matters: A look at LLM-generated group recommendations. 993-998 - Hanne Vandenbroucke, Annelien Smets:
It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender Metrics. 999-1003 - Yi Wu, Daryl Chang, Jennifer She, Zhe Zhao, Li Wei, Lukasz Heldt:
Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction. 1004-1009 - 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:
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations. 1010-1015 - Zeyuan Meng, Zixuan Yi, Iadh Ounis:
Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective Recommendation. 1016-1021 - Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl:
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. 1022-1027 - Pavan Seshadri, Shahrzad Shashaani, Peter Knees:
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning. 1028-1032 - Ramin Raziperchikolaei, Young-joo Chung:
One-class recommendation systems with the hinge pairwise distance loss and orthogonal representations. 1033-1038 - 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:
Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations. 1039-1044 - Zhouhang Xie, Junda Wu, Hyunsik Jeon, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, Julian J. McAuley:
Neighborhood-Based Collaborative Filtering for Conversational Recommendation. 1045-1050 - Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Recommending Personalised Targeted Training Adjustments for Marathon Runners. 1051-1056 - Alessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro:
Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models. 1057-1061 - Elias Entrup, Ralph Ewerth, Anett Hoppe:
Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems. 1062-1066 - Anton Klenitskiy, Anna Volodkevich, Anton Pembek, Alexey Vasilev:
Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations. 1067-1072
Late-breaking results
- Alexander Eggerth, Javier Argota Sánchez-Vaquerizo, Dirk Helbing, Sachit Mahajan:
Democratizing Urban Mobility Through an Open-Source, Multi-Criteria Route Recommendation System. 1073-1078 - Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras, Alessandro Soccol:
KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation. 1079-1084 - Joeran Beel, Lukas Wegmeth, Lien Michiels, Steffen Schulz:
Informed Dataset Selection with 'Algorithm Performance Spaces'. 1085-1090 - Kai Sugahara, Chihiro Yamasaki, Kazushi Okamoto:
Is It Really Complementary? Revisiting Behavior-based Labels for Complementary Recommendation. 1091-1095 - Amanda Aird, Elena Stefancova, Cassidy All, Amy Voida, Martin Homola, Nicholas Mattei, Robin Burke:
Social Choice for Heterogeneous Fairness in Recommendation. 1096-1101 - Vojtech Vancura, Pavel Kordík, Milan Straka:
beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems. 1102-1107 - Thi Ngoc Trang Tran, Seda Polat Erdeniz, Alexander Felfernig, Sebastian Lubos, Merfat El Mansi, Viet-Man Le:
Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems. 1108-1112 - Amir Reza Mohammadi, Andreas Peintner, Michael Müller, Eva Zangerle:
Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating Explainers. 1113-1118 - Jiaye Lin, Shuang Peng, Zhong Zhang, Peilin Zhao:
TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation Tasks. 1119-1124 - Veronica Kecki, Alan Said:
Understanding Fairness in Recommender Systems: A Healthcare Perspective. 1125-1130 - Linus W. Dietz, Sanja Scepanovic, Ke Zhou, Daniele Quercia:
Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities. 1131-1135 - Antonela Tommasel, J. Andres Diaz-Pace:
Leveraging Monte Carlo Tree Search for Group Recommendation. 1136-1141 - Yuuki Tachioka:
User Knowledge Prompt for Sequential Recommendation. 1142-1146 - Ine Coppens, Toon De Pessemier, Luc Martens:
Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity Recommendations. 1147-1151 - Zheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor:
Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems. 1152-1157 - Ahmadou Wagne, Julia Neidhardt:
What to compare? Towards understanding user sessions on price comparison platforms. 1158-1162 - Lukas Wegmeth, Tobias Vente, Joeran Beel:
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets. 1163-1167 - Davide Abbattista, Vito Walter Anelli, Tommaso Di Noia, Craig MacDonald, Aleksandr Vladimirovich Petrov:
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness. 1168-1173
Demonstrations
- Tri Kurniawan Wijaya, Edoardo D'Amico, Gábor Fodor, Manuel V. Loureiro:
Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues. 1174-1176 - Ulysse Maes, Lien Michiels, Annelien Smets:
GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMs. 1177-1179 - Yang Xu, Kuan-Ting Lai, Pengcheng Xiong, Zhong Wu:
Multi-Preview Recommendation via Reinforcement Learning. 1180-1183 - Eduardo Alves da Silva, Leandro Balby Marinho, Edleno Silva de Moura, Altigran Soares da Silva:
A Tool for Explainable Pension Fund Recommendations using Large Language Models. 1184-1186 - Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy:
Stalactite: toolbox for fast prototyping of vertical federated learning systems. 1187-1190 - Alexey Vasilev, Anna Volodkevich, Denis Kulandin, Tatiana Bysheva, Anton Klenitskiy:
RePlay: a Recommendation Framework for Experimentation and Production Use. 1191-1194
Workshops and Challenge
- Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. 1195-1199 - Michael D. Ekstrand, Toshihiro Kamishima, Amifa Raj, Karlijn Dinnissen:
FAccTRec 2024: The 7th Workshop on Responsible Recommendation. 1200-1201 - Andres Ferraro, Lorenzo Porcaro, Peter Knees, Christine Bauer:
MuRS 2024: 2nd Music Recommender Systems Workshop. 1202-1205 - Olivier Jeunen, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang:
CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. 1206-1209 - Himan Abdollahpouri, Tonia Danylenko, Masoud Mansoury, Babak Loni, Daniel Russo, Mihajlo Grbovic:
SURE 2024: Workshop on Strategic and Utility-aware REcommendation. 1210-1212 - Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Saurabh Gupta, Brad Schumitsch, Arnab Bhadury, Ding Tong, Ko-Jen Hsiao, Liang Liu:
VideoRecSys + LargeRecSys 2024. 1213-1215 - Michael D. Ekstrand, Maria Soledad Pera, Alan Said:
AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation. 1216-1218 - Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems (CARS) 2024. 1219-1221 - Toine Bogers, David Graus, Mesut Kaya, Chris Johnson, Jens-Joris Decorte, Tijl De Bie:
Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024). 1222-1226 - Adir Solomon, Tsvi Kuflik, Bracha Shapira, Ido Guy:
RecTemp: Temporal Reasoning in Recommendation Systems. 1227-1228 - Julia Neidhardt, Tsvi Kuflik, Amit Livne, Markus Zanker:
Workshop on Recommenders in Tourism (RecTour) 2024. 1229-1231 - Hanna Hauptmann, Christoph Trattner, Helma Torkamaan:
The 6th International Workshop on Health Recommender Systems. 1232-1236 - Alan Said, Christine Bauer, Eva Zangerle:
Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES). 1237-1238 - Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesco Ricci:
First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024). 1239-1241 - Alain Starke, Sanne Vrijenhoek, Lien Michiels, Johannes Kruse, Nava Tintarev:
NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems. 1242-1244 - Vito Walter Anelli, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker:
Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). 1245-1249 - Yashar Deldjoo, Julian J. McAuley, Scott Sanner, Pablo Castells, Shuai Zhang, Enrico Palumbo:
The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN). 1250-1252 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen:
11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24). 1253-1257 - Benjamin Kille, Andreas Lommatzsch, Célina Treuillier, Vandana Yadav, Özlem Özgöbek:
12th International Workshop on News Recommendation and Analytics (INRA'24). 1258-1261 - Irene Li, Ruihai Dong, Lei Li, Li Chen:
EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language Models. 1262-1264 - Valerio Guarrasi, Federico Siciliano, Fabrizio Silvestri:
RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems. 1265-1269
Tutorials
- Panagiotis Symeonidis:
Deep Recommendation using Graphs. 1270-1271 - Bart P. Knijnenburg, Edward C. Malthouse:
Conducting User Experiments in Recommender Systems. 1272-1273 - Bereket Abera Yilma:
Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and Healthcare. 1274-1276 - Robin Burke, Joseph A. Konstan, Michael D. Ekstrand:
Conducting Recommender Systems User Studies Using POPROX. 1277-1278 - Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello:
Economics of Recommender Systems. 1279-1280 - Zhaocheng Du, Chuhan Wu, Qinglin Jia, Jieming Zhu, Xu Chen:
A Tutorial on Feature Interpretation in Recommender Systems. 1281-1282
Doctoral symposium
- Jia Hua Jeng:
Bridging Viewpoints in News with Recommender Systems. 1283-1289 - Marta Moscati:
Multimodal Representation Learning for High-Quality Recommendations in Cold-Start and Beyond-Accuracy. 1290-1295 - Mahta Bakhshizadeh:
Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender Systems. 1296-1301 - Kathrin Wardatzky:
Evaluating the Pros and Cons of Recommender Systems Explanations. 1302-1307 - Ruixuan Sun:
AI-based Human-Centered Recommender Systems: Empirical Experiments and Research Infrastructure. 1308-1313 - Rachana Mehta:
Integrating Matrix Factorization with Graph based Models. 1314-1317 - Roan Schellingerhout:
Explainable Multi-Stakeholder Job Recommender Systems. 1318-1322 - Mikhail Baklanov:
CEERS: Counterfactual Evaluations of Explanations in Recommender Systems. 1323-1329 - Pavel Merinov:
Towards Sustainable Recommendations in Urban Tourism. 1330-1334 - Brett Binst:
How to Evaluate Serendipity in Recommender Systems: the Need for a Serendiptionnaire. 1335-1341 - Bianca Maria Deconcini:
Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User Engagement. 1342-1347 - Angela Di Fazio:
Enhancing Privacy in Recommender Systems through Differential Privacy Techniques. 1348-1352 - Luan Soares de Souza:
Fairness Explanations in Recommender Systems. 1353-1354 - Larry Donald Preuett:
Learning Personalized Health Recommendations via Offline Reinforcement Learning. 1355-1357 - Neda Afreen:
Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge Graphs. 1358-1360 - Alessandro Petruzzelli:
Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems. 1361-1367 - Karlijn Dinnissen:
Fairness and Transparency in Music Recommender Systems: Improvements for Artists. 1368-1375 - Savvina Daniil:
Bias in Book Recommendation. 1376-1381 - Gaetano Dibenedetto:
A New Perspective in Health Recommendations: Integration of Human Pose Estimation. 1382-1387 - Thomas Elmar Kolb:
Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures. 1388-1394 - Shahrzad Shashaani:
Explainability in Music Recommender System. 1395-1401
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