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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 - </